index int64 0 0 | repo_id stringclasses 596 values | file_path stringlengths 31 168 | content stringlengths 1 6.2M |
|---|---|---|---|
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_faillure = True
print(file)
traceback.print_exc()
print()
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/pinecone/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
test tests integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/pinecone --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_pinecone
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_pinecone -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/pinecone/LICENSE | MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/pinecone/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
yarl = ">=1.0,<2.0"
[package.extras]
speedups = ["Brotli", "aiodns", "brotlicffi"]
[[package]]
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]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
propcache = ">=0.2.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "b19080a915cafa4786cc8238c11e89b05e496912674d8915d22fe1fe85604b22"
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/pinecone/README.md | # langchain-pinecone
This package contains the LangChain integration with Pinecone.
## Installation
```bash
pip install -U langchain-pinecone
```
And you should configure credentials by setting the following environment variables:
- `PINECONE_API_KEY`
- `PINECONE_INDEX_NAME`
## Usage
The `PineconeVectorStore` class exposes the connection to the Pinecone vector store.
```python
from langchain_pinecone import PineconeVectorStore
embeddings = ... # use a LangChain Embeddings class
vectorstore = PineconeVectorStore(embeddings=embeddings)
```
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/pinecone/pyproject.toml | [build-system]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-pinecone"
version = "0.2.1"
description = "An integration package connecting Pinecone and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.mypy]
disallow_untyped_defs = "True"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/pinecone"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-pinecone%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
langchain-core = "^0.3.21"
pinecone = "^5.4.0"
aiohttp = ">=3.9.5,<3.10"
numpy = ">=1.26.0,<2.0.0"
[tool.ruff.lint]
select = [ "E", "F", "I", "T201",]
[tool.coverage.run]
omit = [ "tests/*",]
[tool.pytest.ini_options]
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them",]
asyncio_mode = "auto"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
simsimd = "^5.0.0"
[tool.poetry.group.test.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.test_integration.dependencies.langchain-openai]
path = "../openai"
develop = true
[tool.poetry.group.dev.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.typing.dependencies.langchain-core]
path = "../../core"
develop = true
|
0 | lc_public_repos/langchain/libs/partners/pinecone | lc_public_repos/langchain/libs/partners/pinecone/langchain_pinecone/_utilities.py | from enum import Enum
from typing import List, Union
import numpy as np
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
class DistanceStrategy(str, Enum):
"""Enumerator of the Distance strategies for calculating distances
between vectors."""
EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE"
MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT"
COSINE = "COSINE"
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance."""
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
f"Number of columns in X and Y must be the same. X has shape {X.shape} "
f"and Y has shape {Y.shape}."
)
try:
import simsimd as simd
X = np.array(X, dtype=np.float32)
Y = np.array(Y, dtype=np.float32)
Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
return Z
except ImportError:
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
|
0 | lc_public_repos/langchain/libs/partners/pinecone | lc_public_repos/langchain/libs/partners/pinecone/langchain_pinecone/vectorstores.py | from __future__ import annotations
import logging
import os
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
TypeVar,
)
import numpy as np
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils.iter import batch_iterate
from langchain_core.vectorstores import VectorStore
from pinecone import Pinecone as PineconeClient # type: ignore
from langchain_pinecone._utilities import DistanceStrategy, maximal_marginal_relevance
if TYPE_CHECKING:
from pinecone import Index
logger = logging.getLogger(__name__)
VST = TypeVar("VST", bound=VectorStore)
class PineconeVectorStore(VectorStore):
"""Pinecone vector store integration.
Setup:
Install ``langchain-pinecone`` and set the environment variable ``PINECONE_API_KEY``.
.. code-block:: bash
pip install -qU langchain-pinecone
export PINECONE_API_KEY = "your-pinecone-api-key"
Key init args — indexing params:
embedding: Embeddings
Embedding function to use.
Key init args — client params:
index: Optional[Index]
Index to use.
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
import time
import os
from pinecone import Pinecone, ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
index_name = "langchain-test-index" # change if desired
existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
if index_name not in existing_indexes:
pc.create_index(
name=index_name,
dimension=1536,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
deletion_protection="enabled", # Defaults to "disabled"
)
while not pc.describe_index(index_name).status["ready"]:
time.sleep(1)
index = pc.Index(index_name)
vector_store = PineconeVectorStore(index=index, embedding=OpenAIEmbeddings())
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")
documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
.. code-block:: python
vector_store.delete(ids=["3"])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'bar': 'baz'}]
Search with filter:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'bar': 'baz'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.832268] foo [{'baz': 'bar'}]
Async:
.. code-block:: python
# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)
# delete documents
# await vector_store.adelete(ids=["3"])
# search
# results = vector_store.asimilarity_search(query="thud",k=1)
# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.832268] foo [{'baz': 'bar'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")
.. code-block:: python
[Document(metadata={'bar': 'baz'}, page_content='thud')]
""" # noqa: E501
def __init__(
self,
# setting default params to bypass having to pass in
# the index and embedding objects - manually throw
# exceptions if they are not passed in or set in environment
# (keeping param for backwards compatibility)
index: Optional[Any] = None,
embedding: Optional[Embeddings] = None,
text_key: Optional[str] = "text",
namespace: Optional[str] = None,
distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE,
*,
pinecone_api_key: Optional[str] = None,
index_name: Optional[str] = None,
):
if embedding is None:
raise ValueError("Embedding must be provided")
self._embedding = embedding
if text_key is None:
raise ValueError("Text key must be provided")
self._text_key = text_key
self._namespace = namespace
self.distance_strategy = distance_strategy
if index:
# supports old way of initializing externally
self._index = index
else:
# all internal initialization
_pinecone_api_key = (
pinecone_api_key or os.environ.get("PINECONE_API_KEY") or ""
)
if not _pinecone_api_key:
raise ValueError(
"Pinecone API key must be provided in either `pinecone_api_key` "
"or `PINECONE_API_KEY` environment variable"
)
_index_name = index_name or os.environ.get("PINECONE_INDEX_NAME") or ""
if not _index_name:
raise ValueError(
"Pinecone index name must be provided in either `index_name` "
"or `PINECONE_INDEX_NAME` environment variable"
)
# needs
client = PineconeClient(api_key=_pinecone_api_key, source_tag="langchain")
self._index = client.Index(_index_name)
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embedding
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
*,
async_req: bool = True,
id_prefix: Optional[str] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This is done to avoid memory issues and optimize using HTTP based embeddings.
For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
embedding_chunk_size>1000 and batch_size~64 for best performance.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
namespace: Optional pinecone namespace to add the texts to.
batch_size: Batch size to use when adding the texts to the vectorstore.
embedding_chunk_size: Chunk size to use when embedding the texts.
async_req: Whether runs asynchronously.
id_prefix: Optional string to use as an ID prefix when upserting vectors.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if namespace is None:
namespace = self._namespace
texts = list(texts)
ids = ids or [str(uuid.uuid4()) for _ in texts]
if id_prefix:
ids = [
id_prefix + "#" + id if id_prefix + "#" not in id else id for id in ids
]
metadatas = metadatas or [{} for _ in texts]
for metadata, text in zip(metadatas, texts):
metadata[self._text_key] = text
# For loops to avoid memory issues and optimize when using HTTP based embeddings
# The first loop runs the embeddings, it benefits when using OpenAI embeddings
# The second loops runs the pinecone upsert asynchronously.
for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = self._embedding.embed_documents(chunk_texts)
vector_tuples = zip(chunk_ids, embeddings, chunk_metadatas)
if async_req:
# Runs the pinecone upsert asynchronously.
async_res = [
self._index.upsert(
vectors=batch_vector_tuples,
namespace=namespace,
async_req=async_req,
**kwargs,
)
for batch_vector_tuples in batch_iterate(batch_size, vector_tuples)
]
[res.get() for res in async_res]
else:
self._index.upsert(
vectors=vector_tuples,
namespace=namespace,
async_req=async_req,
**kwargs,
)
return ids
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
return self.similarity_search_by_vector_with_score(
self._embedding.embed_query(query), k=k, filter=filter, namespace=namespace
)
def similarity_search_by_vector_with_score(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to embedding, along with scores."""
if namespace is None:
namespace = self._namespace
docs = []
results = self._index.query(
vector=embedding,
top_k=k,
include_metadata=True,
namespace=namespace,
filter=filter,
)
for res in results["matches"]:
metadata = res["metadata"]
id = res.get("id")
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
docs.append(
(Document(id=id, page_content=text, metadata=metadata), score)
)
else:
logger.warning(
f"Found document with no `{self._text_key}` key. Skipping."
)
return docs
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return pinecone documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, filter=filter, namespace=namespace, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, max_inner_product "
"(dot product), or euclidean"
)
@staticmethod
def _cosine_relevance_score_fn(score: float) -> float:
"""Pinecone returns cosine similarity scores between [-1,1]"""
return (score + 1) / 2
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if namespace is None:
namespace = self._namespace
results = self._index.query(
vector=[embedding],
top_k=fetch_k,
include_values=True,
include_metadata=True,
namespace=namespace,
filter=filter,
)
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
[item["values"] for item in results["matches"]],
k=k,
lambda_mult=lambda_mult,
)
selected = [results["matches"][i]["metadata"] for i in mmr_selected]
return [
Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
for metadata in selected
]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embedding.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter, namespace
)
@classmethod
def get_pinecone_index(
cls,
index_name: Optional[str],
pool_threads: int = 4,
*,
pinecone_api_key: Optional[str] = None,
) -> Index:
"""Return a Pinecone Index instance.
Args:
index_name: Name of the index to use.
pool_threads: Number of threads to use for index upsert.
pinecone_api_key: The api_key of Pinecone.
Returns:
Pinecone Index instance."""
_pinecone_api_key = pinecone_api_key or os.environ.get("PINECONE_API_KEY") or ""
client = PineconeClient(
api_key=_pinecone_api_key, pool_threads=pool_threads, source_tag="langchain"
)
indexes = client.list_indexes()
index_names = [i.name for i in indexes.index_list["indexes"]]
if index_name in index_names:
index = client.Index(index_name)
elif len(index_names) == 0:
raise ValueError(
"No active indexes found in your Pinecone project, "
"are you sure you're using the right Pinecone API key and Environment? "
"Please double check your Pinecone dashboard."
)
else:
raise ValueError(
f"Index '{index_name}' not found in your Pinecone project. "
f"Did you mean one of the following indexes: {', '.join(index_names)}"
)
return index
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 32,
text_key: str = "text",
namespace: Optional[str] = None,
index_name: Optional[str] = None,
upsert_kwargs: Optional[dict] = None,
pool_threads: int = 4,
embeddings_chunk_size: int = 1000,
async_req: bool = True,
*,
id_prefix: Optional[str] = None,
**kwargs: Any,
) -> PineconeVectorStore:
"""Construct Pinecone wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Adds the documents to a provided Pinecone index
This is intended to be a quick way to get started.
The `pool_threads` affects the speed of the upsert operations.
Setup: set the `PINECONE_API_KEY` environment variable to your Pinecone API key.
Example:
.. code-block:: python
from langchain_pinecone import PineconeVectorStore, PineconeEmbeddings
embeddings = PineconeEmbeddings(model="multilingual-e5-large")
index_name = "my-index"
vectorstore = PineconeVectorStore.from_texts(
texts,
index_name=index_name,
embedding=embedding,
namespace=namespace,
)
"""
pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs)
pinecone.add_texts(
texts,
metadatas=metadatas,
ids=ids,
namespace=namespace,
batch_size=batch_size,
embedding_chunk_size=embeddings_chunk_size,
async_req=async_req,
id_prefix=id_prefix,
**(upsert_kwargs or {}),
)
return pinecone
@classmethod
def from_existing_index(
cls,
index_name: str,
embedding: Embeddings,
text_key: str = "text",
namespace: Optional[str] = None,
pool_threads: int = 4,
) -> PineconeVectorStore:
"""Load pinecone vectorstore from index name."""
pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
return cls(pinecone_index, embedding, text_key, namespace)
def delete(
self,
ids: Optional[List[str]] = None,
delete_all: Optional[bool] = None,
namespace: Optional[str] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> None:
"""Delete by vector IDs or filter.
Args:
ids: List of ids to delete.
delete_all: Whether delete all vectors in the index.
filter: Dictionary of conditions to filter vectors to delete.
namespace: Namespace to search in. Default will search in '' namespace.
"""
if namespace is None:
namespace = self._namespace
if delete_all:
self._index.delete(delete_all=True, namespace=namespace, **kwargs)
elif ids is not None:
chunk_size = 1000
for i in range(0, len(ids), chunk_size):
chunk = ids[i : i + chunk_size]
self._index.delete(ids=chunk, namespace=namespace, **kwargs)
elif filter is not None:
self._index.delete(filter=filter, namespace=namespace, **kwargs)
else:
raise ValueError("Either ids, delete_all, or filter must be provided.")
return None
@deprecated(since="0.0.3", removal="1.0.0", alternative="PineconeVectorStore")
class Pinecone(PineconeVectorStore):
"""Deprecated. Use PineconeVectorStore instead."""
pass
|
0 | lc_public_repos/langchain/libs/partners/pinecone | lc_public_repos/langchain/libs/partners/pinecone/langchain_pinecone/embeddings.py | import logging
from typing import Any, Dict, Iterable, List, Optional
import aiohttp
from langchain_core.embeddings import Embeddings
from langchain_core.utils import secret_from_env
from pinecone import Pinecone as PineconeClient # type: ignore[import-untyped]
from pydantic import (
BaseModel,
ConfigDict,
Field,
PrivateAttr,
SecretStr,
model_validator,
)
from typing_extensions import Self
logger = logging.getLogger(__name__)
DEFAULT_BATCH_SIZE = 64
class PineconeEmbeddings(BaseModel, Embeddings):
"""PineconeEmbeddings embedding model.
Example:
.. code-block:: python
from langchain_pinecone import PineconeEmbeddings
model = PineconeEmbeddings(model="multilingual-e5-large")
"""
# Clients
_client: PineconeClient = PrivateAttr(default=None)
_async_client: Optional[aiohttp.ClientSession] = PrivateAttr(default=None)
model: str
"""Model to use for example 'multilingual-e5-large'."""
# Config
batch_size: Optional[int] = None
"""Batch size for embedding documents."""
query_params: Dict = Field(default_factory=dict)
"""Parameters for embedding query."""
document_params: Dict = Field(default_factory=dict)
"""Parameters for embedding document"""
#
dimension: Optional[int] = None
#
show_progress_bar: bool = False
pinecone_api_key: SecretStr = Field(
default_factory=secret_from_env(
"PINECONE_API_KEY",
error_message="Pinecone API key not found. Please set the PINECONE_API_KEY "
"environment variable or pass it via `pinecone_api_key`.",
),
alias="api_key",
)
"""Pinecone API key.
If not provided, will look for the PINECONE_API_KEY environment variable."""
model_config = ConfigDict(
extra="forbid",
populate_by_name=True,
protected_namespaces=(),
)
@property
def async_client(self) -> aiohttp.ClientSession:
"""Lazily initialize the async client."""
if self._async_client is None:
self._async_client = aiohttp.ClientSession(
headers={
"Api-Key": self.pinecone_api_key.get_secret_value(),
"Content-Type": "application/json",
"X-Pinecone-API-Version": "2024-10",
}
)
return self._async_client
@model_validator(mode="before")
@classmethod
def set_default_config(cls, values: dict) -> Any:
"""Set default configuration based on model."""
default_config_map = {
"multilingual-e5-large": {
"batch_size": 96,
"query_params": {"input_type": "query", "truncation": "END"},
"document_params": {"input_type": "passage", "truncation": "END"},
"dimension": 1024,
}
}
model = values.get("model")
if model in default_config_map:
config = default_config_map[model]
for key, value in config.items():
if key not in values:
values[key] = value
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that Pinecone version and credentials exist in environment."""
api_key_str = self.pinecone_api_key.get_secret_value()
client = PineconeClient(api_key=api_key_str, source_tag="langchain")
self._client = client
# Ensure async_client is lazily initialized
_ = self.async_client
return self
def _get_batch_iterator(self, texts: List[str]) -> Iterable:
if self.batch_size is None:
batch_size = DEFAULT_BATCH_SIZE
else:
batch_size = self.batch_size
if self.show_progress_bar:
try:
from tqdm.auto import tqdm # type: ignore
except ImportError as e:
raise ImportError(
"Must have tqdm installed if `show_progress_bar` is set to True. "
"Please install with `pip install tqdm`."
) from e
_iter = tqdm(range(0, len(texts), batch_size))
else:
_iter = range(0, len(texts), batch_size)
return _iter
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
response = self._client.inference.embed(
model=self.model,
parameters=self.document_params,
inputs=texts[i : i + self.batch_size],
)
embeddings.extend([r["values"] for r in response])
return embeddings
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
response = await self._aembed_texts(
model=self.model,
parameters=self.document_params,
texts=texts[i : i + self.batch_size],
)
embeddings.extend([r["values"] for r in response["data"]])
return embeddings
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
return self._client.inference.embed(
model=self.model, parameters=self.query_params, inputs=[text]
)[0]["values"]
async def aembed_query(self, text: str) -> List[float]:
"""Asynchronously embed query text."""
response = await self._aembed_texts(
model=self.model,
parameters=self.document_params,
texts=[text],
)
return response["data"][0]["values"]
async def _aembed_texts(
self, texts: List[str], model: str, parameters: dict
) -> Dict:
data = {
"model": model,
"inputs": [{"text": text} for text in texts],
"parameters": parameters,
}
async with self.async_client.post(
"https://api.pinecone.io/embed", json=data
) as response:
response_data = await response.json(content_type=None)
return response_data
|
0 | lc_public_repos/langchain/libs/partners/pinecone | lc_public_repos/langchain/libs/partners/pinecone/langchain_pinecone/__init__.py | from langchain_pinecone.embeddings import PineconeEmbeddings
from langchain_pinecone.vectorstores import Pinecone, PineconeVectorStore
__all__ = [
"PineconeEmbeddings",
"PineconeVectorStore",
"Pinecone",
]
|
0 | lc_public_repos/langchain/libs/partners/pinecone/tests | lc_public_repos/langchain/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py | import os
import time
import uuid
from typing import List
import numpy as np
import pinecone # type: ignore
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings # type: ignore[import-not-found]
from pinecone import PodSpec
from pytest_mock import MockerFixture # type: ignore[import-not-found]
from langchain_pinecone import PineconeVectorStore
INDEX_NAME = "langchain-test-index" # name of the index
NAMESPACE_NAME = "langchain-test-namespace" # name of the namespace
DIMENSION = 1536 # dimension of the embeddings
DEFAULT_SLEEP = 20
class TestPinecone:
index: "pinecone.Index"
@classmethod
def setup_class(cls) -> None:
import pinecone
client = pinecone.Pinecone(api_key=os.environ["PINECONE_API_KEY"])
index_list = client.list_indexes()
for i in index_list:
if i["name"] == INDEX_NAME:
client.delete_index(INDEX_NAME)
break
if len(index_list) > 0:
time.sleep(DEFAULT_SLEEP) # prevent race with creation
client.create_index(
name=INDEX_NAME,
dimension=DIMENSION,
metric="cosine",
spec=PodSpec(environment="gcp-starter"),
)
cls.index = client.Index(INDEX_NAME)
# insure the index is empty
index_stats = cls.index.describe_index_stats()
assert index_stats["dimension"] == DIMENSION
if index_stats["namespaces"].get(NAMESPACE_NAME) is not None:
assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == 0
@classmethod
def teardown_class(cls) -> None:
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
@pytest.fixture(autouse=True)
def setup(self) -> None:
# delete all the vectors in the index
print("called") # noqa: T201
try:
self.index.delete(delete_all=True, namespace=NAMESPACE_NAME)
time.sleep(DEFAULT_SLEEP) # prevent race condition with previous step
except Exception:
# if namespace not found
pass
@pytest.fixture
def embedding_openai(self) -> OpenAIEmbeddings:
return OpenAIEmbeddings()
@pytest.fixture
def texts(self) -> List[str]:
return ["foo", "bar", "baz"]
def test_from_texts(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
docsearch = PineconeVectorStore.from_texts(
texts=texts,
embedding=embedding_openai,
index_name=INDEX_NAME,
namespace=NAMESPACE_NAME,
)
time.sleep(DEFAULT_SLEEP) # prevent race condition
output = docsearch.similarity_search(unique_id, k=1, namespace=NAMESPACE_NAME)
output[0].id = None # overwrite ID for ease of comparison
assert output == [Document(page_content=needs)]
def test_from_texts_with_metadatas(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts = [needs] + texts
metadatas = [{"page": i} for i in range(len(texts))]
namespace = f"{NAMESPACE_NAME}-md"
docsearch = PineconeVectorStore.from_texts(
texts,
embedding_openai,
index_name=INDEX_NAME,
metadatas=metadatas,
namespace=namespace,
)
time.sleep(DEFAULT_SLEEP) # prevent race condition
output = docsearch.similarity_search(needs, k=1, namespace=namespace)
output[0].id = None
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}?
assert output == [Document(page_content=needs, metadata={"page": 0.0})]
def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> None:
"""Test end to end construction and search with scores and IDs."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
print("metadatas", metadatas) # noqa: T201
docsearch = PineconeVectorStore.from_texts(
texts,
embedding_openai,
index_name=INDEX_NAME,
metadatas=metadatas,
namespace=NAMESPACE_NAME,
)
print(texts) # noqa: T201
time.sleep(DEFAULT_SLEEP) # prevent race condition
output = docsearch.similarity_search_with_score(
"foo", k=3, namespace=NAMESPACE_NAME
)
docs = [o[0] for o in output]
scores = [o[1] for o in output]
sorted_documents = sorted(docs, key=lambda x: x.metadata["page"])
print(sorted_documents) # noqa: T201
for document in sorted_documents:
document.id = None # overwrite IDs for ease of comparison
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc???
assert sorted_documents == [
Document(page_content="foo", metadata={"page": 0.0}),
Document(page_content="bar", metadata={"page": 1.0}),
Document(page_content="baz", metadata={"page": 2.0}),
]
assert scores[0] > scores[1] > scores[2]
def test_from_existing_index_with_namespaces(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test that namespaces are properly handled."""
# Create two indexes with the same name but different namespaces
texts_1 = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts_1))]
PineconeVectorStore.from_texts(
texts_1,
embedding_openai,
index_name=INDEX_NAME,
metadatas=metadatas,
namespace=f"{INDEX_NAME}-1",
)
texts_2 = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts_2))]
PineconeVectorStore.from_texts(
texts_2,
embedding_openai,
index_name=INDEX_NAME,
metadatas=metadatas,
namespace=f"{INDEX_NAME}-2",
)
time.sleep(DEFAULT_SLEEP) # prevent race condition
# Search with namespace
docsearch = PineconeVectorStore.from_existing_index(
index_name=INDEX_NAME,
embedding=embedding_openai,
namespace=f"{INDEX_NAME}-1",
)
output = docsearch.similarity_search("foo", k=20, namespace=f"{INDEX_NAME}-1")
# check that we don't get results from the other namespace
page_contents = sorted(set([o.page_content for o in output]))
assert all(content in ["foo", "bar", "baz"] for content in page_contents)
assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents)
def test_add_documents_with_ids(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
ids = [uuid.uuid4().hex for _ in range(len(texts))]
PineconeVectorStore.from_texts(
texts=texts,
ids=ids,
embedding=embedding_openai,
index_name=INDEX_NAME,
namespace=NAMESPACE_NAME,
)
time.sleep(DEFAULT_SLEEP) # prevent race condition
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == len(texts)
ids_1 = [uuid.uuid4().hex for _ in range(len(texts))]
PineconeVectorStore.from_texts(
texts=[t + "-1" for t in texts],
ids=ids_1,
embedding=embedding_openai,
index_name=INDEX_NAME,
namespace=NAMESPACE_NAME,
)
time.sleep(DEFAULT_SLEEP) # prevent race condition
index_stats = self.index.describe_index_stats()
assert (
index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == len(texts) * 2
)
# only focused on this namespace now
# assert index_stats["total_vector_count"] == len(texts) * 2
@pytest.mark.xfail(reason="relevance score just over 1")
def test_relevance_score_bound(self, embedding_openai: OpenAIEmbeddings) -> None:
"""Ensures all relevance scores are between 0 and 1."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = PineconeVectorStore.from_texts(
texts,
embedding_openai,
index_name=INDEX_NAME,
metadatas=metadatas,
)
# wait for the index to be ready
time.sleep(DEFAULT_SLEEP)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
print(output) # noqa: T201
assert all(
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
)
@pytest.mark.skipif(reason="slow to run for benchmark")
@pytest.mark.parametrize(
"pool_threads,batch_size,embeddings_chunk_size,data_multiplier",
[
(
1,
32,
32,
1000,
), # simulate single threaded with embeddings_chunk_size = batch_size = 32
(
1,
32,
1000,
1000,
), # simulate single threaded with embeddings_chunk_size = 1000
(
4,
32,
1000,
1000,
), # simulate 4 threaded with embeddings_chunk_size = 1000
(20, 64, 5000, 1000),
], # simulate 20 threaded with embeddings_chunk_size = 5000
)
def test_from_texts_with_metadatas_benchmark(
self,
pool_threads: int,
batch_size: int,
embeddings_chunk_size: int,
data_multiplier: int,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
) -> None:
"""Test end to end construction and search."""
texts = [document.page_content for document in documents] * data_multiplier
uuids = [uuid.uuid4().hex for _ in range(len(texts))]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = PineconeVectorStore.from_texts(
texts,
embedding_openai,
ids=uuids,
metadatas=metadatas,
index_name=INDEX_NAME,
namespace=NAMESPACE_NAME,
pool_threads=pool_threads,
batch_size=batch_size,
embeddings_chunk_size=embeddings_chunk_size,
)
query = "What did the president say about Ketanji Brown Jackson"
_ = docsearch.similarity_search(query, k=1, namespace=NAMESPACE_NAME)
@pytest.fixture
def mock_pool_not_supported(self, mocker: MockerFixture) -> None:
"""
This is the error thrown when multiprocessing is not supported.
See https://github.com/langchain-ai/langchain/issues/11168
"""
mocker.patch(
"multiprocessing.synchronize.SemLock.__init__",
side_effect=OSError(
"FileNotFoundError: [Errno 2] No such file or directory"
),
)
@pytest.mark.usefixtures("mock_pool_not_supported")
def test_that_async_freq_uses_multiprocessing(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
with pytest.raises(OSError):
PineconeVectorStore.from_texts(
texts=texts,
embedding=embedding_openai,
index_name=INDEX_NAME,
namespace=NAMESPACE_NAME,
async_req=True,
)
@pytest.mark.usefixtures("mock_pool_not_supported")
def test_that_async_freq_false_enabled_singlethreading(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
PineconeVectorStore.from_texts(
texts=texts,
embedding=embedding_openai,
index_name=INDEX_NAME,
namespace=NAMESPACE_NAME,
async_req=False,
)
|
0 | lc_public_repos/langchain/libs/partners/pinecone/tests | lc_public_repos/langchain/libs/partners/pinecone/tests/integration_tests/test_embeddings.py | import time
import pytest
from langchain_core.documents import Document
from pinecone import Pinecone, ServerlessSpec # type: ignore
from langchain_pinecone import PineconeEmbeddings, PineconeVectorStore
DIMENSION = 1024
INDEX_NAME = "langchain-pinecone-embeddings"
MODEL = "multilingual-e5-large"
@pytest.fixture()
def embd_client() -> PineconeEmbeddings:
return PineconeEmbeddings(model=MODEL)
@pytest.fixture
def pc() -> Pinecone:
return Pinecone()
@pytest.fixture()
def pc_index(pc: Pinecone) -> Pinecone.Index:
if INDEX_NAME not in [index["name"] for index in pc.list_indexes()]:
pc.create_index(
name=INDEX_NAME,
dimension=DIMENSION,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
while not pc.describe_index(INDEX_NAME).status["ready"]:
time.sleep(1)
yield pc.Index(INDEX_NAME)
pc.delete_index(INDEX_NAME)
def test_embed_query(embd_client: PineconeEmbeddings) -> None:
out = embd_client.embed_query("Hello, world!")
assert isinstance(out, list)
assert len(out) == DIMENSION
async def test_aembed_query(embd_client: PineconeEmbeddings) -> None:
out = await embd_client.aembed_query("Hello, world!")
assert isinstance(out, list)
assert len(out) == DIMENSION
def test_embed_documents(embd_client: PineconeEmbeddings) -> None:
out = embd_client.embed_documents(["Hello, world!", "This is a test."])
assert isinstance(out, list)
assert len(out) == 2
assert len(out[0]) == DIMENSION
async def test_aembed_documents(embd_client: PineconeEmbeddings) -> None:
out = await embd_client.aembed_documents(["Hello, world!", "This is a test."])
assert isinstance(out, list)
assert len(out) == 2
assert len(out[0]) == DIMENSION
def test_vector_store(
embd_client: PineconeEmbeddings, pc_index: Pinecone.Index
) -> None:
vectorstore = PineconeVectorStore(index_name=INDEX_NAME, embedding=embd_client)
vectorstore.add_documents([Document("Hello, world!"), Document("This is a test.")])
time.sleep(5)
resp = vectorstore.similarity_search(query="hello")
assert len(resp) == 2
|
0 | lc_public_repos/langchain/libs/partners/pinecone/tests | lc_public_repos/langchain/libs/partners/pinecone/tests/integration_tests/test_compile.py | import pytest # type: ignore[import-not-found]
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/partners/pinecone/tests | lc_public_repos/langchain/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py | from unittest.mock import Mock
from langchain_pinecone.vectorstores import Pinecone, PineconeVectorStore
def test_initialization() -> None:
"""Test integration vectorstore initialization."""
# mock index
index = Mock()
embedding = Mock()
text_key = "xyz"
Pinecone(index, embedding, text_key)
def test_id_prefix() -> None:
"""Test integration of the id_prefix parameter."""
embedding = Mock()
embedding.embed_documents = Mock(return_value=[0.1, 0.2, 0.3, 0.4, 0.5])
index = Mock()
index.upsert = Mock(return_value=None)
text_key = "testing"
vectorstore = PineconeVectorStore(index, embedding, text_key)
texts = ["alpha", "beta", "gamma", "delta", "epsilon"]
id_prefix = "testing_prefixes"
vectorstore.add_texts(texts, id_prefix=id_prefix, async_req=False)
|
0 | lc_public_repos/langchain/libs/partners/pinecone/tests | lc_public_repos/langchain/libs/partners/pinecone/tests/unit_tests/test_imports.py | from langchain_pinecone import __all__
EXPECTED_ALL = [
"PineconeVectorStore",
"Pinecone",
"PineconeEmbeddings",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/pinecone/tests | lc_public_repos/langchain/libs/partners/pinecone/tests/unit_tests/test_embeddings.py | from langchain_core.utils import convert_to_secret_str
from langchain_pinecone import PineconeEmbeddings
API_KEY = convert_to_secret_str("NOT_A_VALID_KEY")
MODEL_NAME = "multilingual-e5-large"
def test_default_config() -> None:
e = PineconeEmbeddings(
pinecone_api_key=API_KEY, # type: ignore[call-arg]
model=MODEL_NAME,
)
assert e.batch_size == 96
def test_default_config_with_api_key() -> None:
e = PineconeEmbeddings(api_key=API_KEY, model=MODEL_NAME)
assert e.batch_size == 96
def test_custom_config() -> None:
e = PineconeEmbeddings(
pinecone_api_key=API_KEY, # type: ignore[call-arg]
model=MODEL_NAME,
batch_size=128,
)
assert e.batch_size == 128
|
0 | lc_public_repos/langchain/libs/partners/pinecone | lc_public_repos/langchain/libs/partners/pinecone/scripts/lint_imports.sh | #!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi
|
0 | lc_public_repos/langchain/libs/partners/pinecone | lc_public_repos/langchain/libs/partners/pinecone/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file) # noqa: T201
traceback.print_exc()
print() # noqa: T201
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/nomic/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_tests: TEST_FILE = tests/integration_tests/
test integration_tests:
poetry run pytest $(TEST_FILE)
tests:
poetry run pytest $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/nomic --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_nomic
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_nomic -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/nomic/LICENSE | MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/nomic/poetry.lock | # This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
files = [
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{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
]
[[package]]
name = "anyio"
version = "4.6.2.post1"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
{file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
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]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "attrs"
version = "24.2.0"
description = "Classes Without Boilerplate"
optional = false
python-versions = ">=3.7"
files = [
{file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
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[package.extras]
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
[[package]]
name = "certifi"
version = "2024.8.30"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
{file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
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[[package]]
name = "charset-normalizer"
version = "3.4.0"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
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python-versions = ">=3.7.0"
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|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/nomic/README.md | # langchain-nomic
This package contains the LangChain integration with Nomic
## Installation
```bash
pip install -U langchain-nomic
```
And you should configure credentials by setting the following environment variables:
* `NOMIC_API_KEY`: your nomic API key
## Embeddings
`NomicEmbeddings` class exposes embeddings from Nomic.
```python
from langchain_nomic import NomicEmbeddings
embeddings = NomicEmbeddings()
embeddings.embed_query("What is the meaning of life?") |
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/nomic/pyproject.toml | [tool.poetry]
name = "langchain-nomic"
version = "0.1.4"
description = "An integration package connecting Nomic and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/nomic"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-nomic%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14"
nomic = "^3.1.2"
pillow = "^10.3.0"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
langchain-core = { path = "../../core", develop = true }
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.24.0"
python = "<3.12"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.26.0"
python = ">=3.12"
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.lint.dependencies]
ruff = "^0.1.5"
[tool.poetry.group.typing.dependencies]
mypy = "^0.991"
langchain-core = { path = "../../core", develop = true }
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
langchain-core = { path = "../../core", develop = true }
[tool.ruff.lint]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
"T201", # print
]
[tool.mypy]
disallow_untyped_defs = "True"
[tool.coverage.run]
omit = ["tests/*"]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.pytest.ini_options]
# --strict-markers will raise errors on unknown marks.
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
#
# https://docs.pytest.org/en/7.1.x/reference/reference.html
# --strict-config any warnings encountered while parsing the `pytest`
# section of the configuration file raise errors.
#
# https://github.com/tophat/syrupy
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
# Registering custom markers.
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
markers = [
"requires: mark tests as requiring a specific library",
"compile: mark placeholder test used to compile integration tests without running them",
]
asyncio_mode = "auto"
|
0 | lc_public_repos/langchain/libs/partners/nomic | lc_public_repos/langchain/libs/partners/nomic/langchain_nomic/embeddings.py | import os
from typing import List, Literal, Optional, overload
import nomic # type: ignore[import]
from langchain_core.embeddings import Embeddings
from nomic import embed
class NomicEmbeddings(Embeddings):
"""NomicEmbeddings embedding model.
Example:
.. code-block:: python
from langchain_nomic import NomicEmbeddings
model = NomicEmbeddings()
"""
@overload
def __init__(
self,
*,
model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ...,
inference_mode: Literal["remote"] = ...,
):
...
@overload
def __init__(
self,
*,
model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ...,
inference_mode: Literal["local", "dynamic"],
device: Optional[str] = ...,
):
...
@overload
def __init__(
self,
*,
model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ...,
inference_mode: str,
device: Optional[str] = ...,
):
...
def __init__(
self,
*,
model: str,
nomic_api_key: Optional[str] = None,
dimensionality: Optional[int] = None,
inference_mode: str = "remote",
device: Optional[str] = None,
vision_model: Optional[str] = None,
):
"""Initialize NomicEmbeddings model.
Args:
model: model name
nomic_api_key: optionally, set the Nomic API key. Uses the NOMIC_API_KEY
environment variable by default.
dimensionality: The embedding dimension, for use with Matryoshka-capable
models. Defaults to full-size.
inference_mode: How to generate embeddings. One of `remote`, `local`
(Embed4All), or `dynamic` (automatic). Defaults to `remote`.
device: The device to use for local embeddings. Choices include
`cpu`, `gpu`, `nvidia`, `amd`, or a specific device name. See
the docstring for `GPT4All.__init__` for more info. Typically
defaults to CPU. Do not use on macOS.
"""
_api_key = nomic_api_key or os.environ.get("NOMIC_API_KEY")
if _api_key:
nomic.login(_api_key)
self.model = model
self.dimensionality = dimensionality
self.inference_mode = inference_mode
self.device = device
self.vision_model = vision_model
def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]:
"""Embed texts.
Args:
texts: list of texts to embed
task_type: the task type to use when embedding. One of `search_query`,
`search_document`, `classification`, `clustering`
"""
output = embed.text(
texts=texts,
model=self.model,
task_type=task_type,
dimensionality=self.dimensionality,
inference_mode=self.inference_mode,
device=self.device,
)
return output["embeddings"]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs.
Args:
texts: list of texts to embed as documents
"""
return self.embed(
texts=texts,
task_type="search_document",
)
def embed_query(self, text: str) -> List[float]:
"""Embed query text.
Args:
text: query text
"""
return self.embed(
texts=[text],
task_type="search_query",
)[0]
def embed_image(self, uris: List[str]) -> List[List[float]]:
return embed.image(
images=uris,
model=self.vision_model,
)["embeddings"]
|
0 | lc_public_repos/langchain/libs/partners/nomic | lc_public_repos/langchain/libs/partners/nomic/langchain_nomic/__init__.py | from langchain_nomic.embeddings import NomicEmbeddings
__all__ = ["NomicEmbeddings"]
|
0 | lc_public_repos/langchain/libs/partners/nomic/tests | lc_public_repos/langchain/libs/partners/nomic/tests/integration_tests/test_embeddings.py | """Test Nomic embeddings."""
from langchain_nomic.embeddings import NomicEmbeddings
def test_langchain_nomic_embedding_documents() -> None:
"""Test nomic embeddings."""
documents = ["foo bar"]
embedding = NomicEmbeddings(model="nomic-embed-text-v1")
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) > 0
def test_langchain_nomic_embedding_query() -> None:
"""Test nomic embeddings."""
document = "foo bar"
embedding = NomicEmbeddings(model="nomic-embed-text-v1")
output = embedding.embed_query(document)
assert len(output) > 0
def test_langchain_nomic_embedding_dimensionality() -> None:
"""Test nomic embeddings."""
documents = ["foo bar"]
embedding = NomicEmbeddings(model="nomic-embed-text-v1.5", dimensionality=256)
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 256
|
0 | lc_public_repos/langchain/libs/partners/nomic/tests | lc_public_repos/langchain/libs/partners/nomic/tests/integration_tests/test_compile.py | import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/partners/nomic/tests | lc_public_repos/langchain/libs/partners/nomic/tests/unit_tests/test_imports.py | from langchain_nomic import __all__
EXPECTED_ALL = [
"NomicEmbeddings",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/nomic/tests | lc_public_repos/langchain/libs/partners/nomic/tests/unit_tests/test_embeddings.py | """Test embedding model integration."""
from langchain_nomic.embeddings import NomicEmbeddings
def test_initialization() -> None:
"""Test embedding model initialization."""
NomicEmbeddings(model="nomic-embed-text-v1")
|
0 | lc_public_repos/langchain/libs/partners/nomic | lc_public_repos/langchain/libs/partners/nomic/scripts/lint_imports.sh | #!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi
|
0 | lc_public_repos/langchain/libs/partners/nomic | lc_public_repos/langchain/libs/partners/nomic/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file) # noqa: T201
traceback.print_exc()
print() # noqa: T201
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/Makefile |
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/cli --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_cli
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
test tests: _test _e2e_test
PYTHON = .venv/bin/python
_test:
poetry run pytest tests
# custom integration testing for cli integration flow
# currently ignores vectorstores test because lacks implementation
_e2e_test:
rm -rf .integration_test
mkdir .integration_test
cd .integration_test && \
python3 -m venv .venv && \
$(PYTHON) -m pip install --upgrade poetry && \
$(PYTHON) -m pip install -e .. && \
$(PYTHON) -m langchain_cli.cli integration new --name parrot-link --name-class ParrotLink && \
cd langchain-parrot-link && \
poetry install --with lint,typing,test && \
poetry run pip install -e ../../../standard-tests && \
make format lint tests && \
poetry install --with test_integration && \
rm tests/integration_tests/test_vectorstores.py && \
make integration_test
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/LICENSE | MIT License
Copyright (c) LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/DOCS.md | # `langchain`
**Usage**:
```console
$ langchain [OPTIONS] COMMAND [ARGS]...
```
**Options**:
* `--help`: Show this message and exit.
* `-v, --version`: Print current CLI version.
**Commands**:
* `app`: Manage LangChain apps
* `serve`: Start the LangServe app, whether it's a...
* `template`: Develop installable templates.
## `langchain app`
Manage LangChain apps
**Usage**:
```console
$ langchain app [OPTIONS] COMMAND [ARGS]...
```
**Options**:
* `--help`: Show this message and exit.
**Commands**:
* `add`: Adds the specified template to the current...
* `new`: Create a new LangServe application.
* `remove`: Removes the specified package from the...
* `serve`: Starts the LangServe app.
### `langchain app add`
Adds the specified template to the current LangServe app.
e.g.:
langchain app add extraction-openai-functions
langchain app add git+ssh://git@github.com/efriis/simple-pirate.git
**Usage**:
```console
$ langchain app add [OPTIONS] [DEPENDENCIES]...
```
**Arguments**:
* `[DEPENDENCIES]...`: The dependency to add
**Options**:
* `--api-path TEXT`: API paths to add
* `--project-dir PATH`: The project directory
* `--repo TEXT`: Install templates from a specific github repo instead
* `--branch TEXT`: Install templates from a specific branch
* `--help`: Show this message and exit.
### `langchain app new`
Create a new LangServe application.
**Usage**:
```console
$ langchain app new [OPTIONS] NAME
```
**Arguments**:
* `NAME`: The name of the folder to create [required]
**Options**:
* `--package TEXT`: Packages to seed the project with
* `--help`: Show this message and exit.
### `langchain app remove`
Removes the specified package from the current LangServe app.
**Usage**:
```console
$ langchain app remove [OPTIONS] API_PATHS...
```
**Arguments**:
* `API_PATHS...`: The API paths to remove [required]
**Options**:
* `--help`: Show this message and exit.
### `langchain app serve`
Starts the LangServe app.
**Usage**:
```console
$ langchain app serve [OPTIONS]
```
**Options**:
* `--port INTEGER`: The port to run the server on
* `--host TEXT`: The host to run the server on
* `--app TEXT`: The app to run, e.g. `app.server:app`
* `--help`: Show this message and exit.
## `langchain serve`
Start the LangServe app, whether it's a template or an app.
**Usage**:
```console
$ langchain serve [OPTIONS]
```
**Options**:
* `--port INTEGER`: The port to run the server on
* `--host TEXT`: The host to run the server on
* `--help`: Show this message and exit.
## `langchain template`
Develop installable templates.
**Usage**:
```console
$ langchain template [OPTIONS] COMMAND [ARGS]...
```
**Options**:
* `--help`: Show this message and exit.
**Commands**:
* `new`: Creates a new template package.
* `serve`: Starts a demo app for this template.
### `langchain template new`
Creates a new template package.
**Usage**:
```console
$ langchain template new [OPTIONS] NAME
```
**Arguments**:
* `NAME`: The name of the folder to create [required]
**Options**:
* `--with-poetry / --no-poetry`: Don't run poetry install [default: no-poetry]
* `--help`: Show this message and exit.
### `langchain template serve`
Starts a demo app for this template.
**Usage**:
```console
$ langchain template serve [OPTIONS]
```
**Options**:
* `--port INTEGER`: The port to run the server on
* `--host TEXT`: The host to run the server on
* `--help`: Show this message and exit.
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/CONTRIBUTING.md | # Contributing to langchain-cli
Update CLI versions with `poe bump` to ensure that version commands display correctly.
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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name = "aiohappyeyeballs"
version = "2.4.4"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
name = "aiohttp"
version = "3.11.9"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.9"
files = [
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]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
propcache = ">=0.2.0"
[extras]
serve = []
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "c666eaa9945394483db2cf56ec2c147869b2fcefb767184c83c4f0e2f211ea2b"
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/README.md | # langchain-cli
This package implements the official CLI for LangChain. Right now, it is most useful
for getting started with LangChain Templates!
[CLI Docs](https://github.com/langchain-ai/langchain/blob/master/libs/cli/DOCS.md)
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/pyproject.toml | [tool.poetry]
name = "langchain-cli"
version = "0.0.34"
description = "CLI for interacting with LangChain"
authors = ["Erick Friis <erick@langchain.dev>"]
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-cli%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
typer = { extras = ["all"], version = "^0.9.0" }
gitpython = "^3"
langserve = { extras = ["all"], version = ">=0.0.51" }
uvicorn = ">=0.23,<1.0"
tomlkit = ">=0.12"
gritql = "^0.1.1"
[tool.poetry.scripts]
langchain = "langchain_cli.cli:app"
langchain-cli = "langchain_cli.cli:app"
[tool.poetry.group.dev.dependencies]
pytest = "^7.4.2"
pytest-watch = "^4.2.0"
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
mypy = "^1.13.0"
[tool.poetry.group.test.dependencies]
langchain = {path = "../langchain", develop = true}
[tool.poetry.group.typing.dependencies]
langchain = {path = "../langchain", develop = true}
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.extras]
# For langserve
serve = []
[tool.ruff.lint]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
"T201", # print
]
[tool.mypy]
exclude = [
"langchain_cli/integration_template",
"langchain_cli/package_template",
]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
|
0 | lc_public_repos/langchain/libs | lc_public_repos/langchain/libs/cli/poetry.toml | [virtualenvs]
in-project = true
|
0 | lc_public_repos/langchain/libs/cli/tests | lc_public_repos/langchain/libs/cli/tests/integration_tests/test_compile.py | import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/cli/tests | lc_public_repos/langchain/libs/cli/tests/unit_tests/test_events.py | import pytest
from langchain_cli.utils.events import EventDict, create_events
@pytest.mark.xfail(reason="Unknown reason")
def test_create_events() -> None:
assert create_events(
[EventDict(event="Test Event", properties={"test": "test"})]
) == {"status": "success"}
|
0 | lc_public_repos/langchain/libs/cli/tests | lc_public_repos/langchain/libs/cli/tests/unit_tests/test_utils.py | from typing import Dict, Optional
import pytest
from langchain_cli.constants import (
DEFAULT_GIT_REF,
DEFAULT_GIT_REPO,
DEFAULT_GIT_SUBDIRECTORY,
)
from langchain_cli.utils.git import DependencySource, parse_dependency_string
def _assert_dependency_equals(
dep: DependencySource,
*,
git: Optional[str] = None,
ref: Optional[str] = None,
subdirectory: Optional[str] = None,
event_metadata: Optional[Dict] = None,
) -> None:
assert dep["git"] == git
assert dep["ref"] == ref
assert dep["subdirectory"] == subdirectory
if event_metadata is not None:
assert dep["event_metadata"] == event_metadata
def test_dependency_string() -> None:
_assert_dependency_equals(
parse_dependency_string(
"git+ssh://git@github.com/efriis/myrepo.git", None, None, None
),
git="ssh://git@github.com/efriis/myrepo.git",
ref=None,
subdirectory=None,
)
_assert_dependency_equals(
parse_dependency_string(
"git+https://github.com/efriis/myrepo.git#subdirectory=src",
None,
None,
None,
),
git="https://github.com/efriis/myrepo.git",
subdirectory="src",
ref=None,
)
_assert_dependency_equals(
parse_dependency_string(
"git+ssh://git@github.com:efriis/myrepo.git#develop", None, None, None
),
git="ssh://git@github.com:efriis/myrepo.git",
ref="develop",
subdirectory=None,
)
# also support a slash in ssh
_assert_dependency_equals(
parse_dependency_string(
"git+ssh://git@github.com/efriis/myrepo.git#develop", None, None, None
),
git="ssh://git@github.com/efriis/myrepo.git",
ref="develop",
subdirectory=None,
)
# looks like poetry supports both an @ and a #
_assert_dependency_equals(
parse_dependency_string(
"git+ssh://git@github.com:efriis/myrepo.git@develop", None, None, None
),
git="ssh://git@github.com:efriis/myrepo.git",
ref="develop",
subdirectory=None,
)
_assert_dependency_equals(
parse_dependency_string("simple-pirate", None, None, None),
git=DEFAULT_GIT_REPO,
subdirectory=f"{DEFAULT_GIT_SUBDIRECTORY}/simple-pirate",
ref=DEFAULT_GIT_REF,
)
def test_dependency_string_both() -> None:
_assert_dependency_equals(
parse_dependency_string(
"git+https://github.com/efriis/myrepo.git@branch#subdirectory=src",
None,
None,
None,
),
git="https://github.com/efriis/myrepo.git",
subdirectory="src",
ref="branch",
)
def test_dependency_string_invalids() -> None:
# expect error for wrong order
with pytest.raises(ValueError):
parse_dependency_string(
"git+https://github.com/efriis/myrepo.git#subdirectory=src@branch",
None,
None,
None,
)
# expect error for @subdirectory
def test_dependency_string_edge_case() -> None:
# weird unsolvable edge case of
# git+ssh://a@b
# this could be a ssh dep with user=a, and default ref
# or a ssh dep at a with ref=b.
# in this case, assume the first case (be greedy with the '@')
_assert_dependency_equals(
parse_dependency_string("git+ssh://a@b", None, None, None),
git="ssh://a@b",
subdirectory=None,
ref=None,
)
# weird one that is actually valid
_assert_dependency_equals(
parse_dependency_string(
"git+https://github.com/efriis/myrepo.git@subdirectory=src",
None,
None,
None,
),
git="https://github.com/efriis/myrepo.git",
subdirectory=None,
ref="subdirectory=src",
)
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/file.py | from __future__ import annotations
class File:
def __init__(self, name: str, content: list[str] | None = None) -> None:
self.name = name
self.content = "\n".join(content or [])
def __eq__(self, __value: object) -> bool:
if not isinstance(__value, File):
return NotImplemented
if self.name != __value.name:
return False
return self.content == __value.content
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/test_cli.py | # ruff: noqa: E402
from __future__ import annotations
import pytest
pytest.importorskip("gritql")
import difflib
from pathlib import Path
from typer.testing import CliRunner
from langchain_cli.cli import app
from tests.unit_tests.migrate.cli_runner.cases import before, expected
from tests.unit_tests.migrate.cli_runner.folder import Folder
def find_issue(current: Folder, expected: Folder) -> str:
for current_file, expected_file in zip(current.files, expected.files):
if current_file != expected_file:
if current_file.name != expected_file.name:
return (
f"Files have "
f"different names: {current_file.name} != {expected_file.name}"
)
if isinstance(current_file, Folder) and isinstance(expected_file, Folder):
return find_issue(current_file, expected_file)
elif isinstance(current_file, Folder) or isinstance(expected_file, Folder):
return (
f"One of the files is a "
f"folder: {current_file.name} != {expected_file.name}"
)
return "\n".join(
difflib.unified_diff(
current_file.content.splitlines(),
expected_file.content.splitlines(),
fromfile=current_file.name,
tofile=expected_file.name,
)
)
return "Unknown"
@pytest.mark.xfail(reason="grit may not be installed in env")
def test_command_line(tmp_path: Path) -> None:
runner = CliRunner()
with runner.isolated_filesystem(temp_dir=tmp_path) as td:
before.create_structure(root=Path(td))
# The input is used to force through the confirmation.
result = runner.invoke(app, ["migrate", before.name, "--force"])
assert result.exit_code == 0, result.output
after = Folder.from_structure(Path(td) / before.name)
assert after == expected, find_issue(after, expected)
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/case.py | from __future__ import annotations
from dataclasses import dataclass
from .file import File
from .folder import Folder
@dataclass
class Case:
source: Folder | File
expected: Folder | File
name: str
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/folder.py | from __future__ import annotations
from pathlib import Path
from .file import File
class Folder:
def __init__(self, name: str, *files: Folder | File) -> None:
self.name = name
self._files = files
@property
def files(self) -> list[Folder | File]:
return sorted(self._files, key=lambda f: f.name)
def create_structure(self, root: Path) -> None:
path = root / self.name
path.mkdir()
for file in self.files:
if isinstance(file, Folder):
file.create_structure(path)
else:
(path / file.name).write_text(file.content, encoding="utf-8")
@classmethod
def from_structure(cls, root: Path) -> Folder:
name = root.name
files: list[File | Folder] = []
for path in root.iterdir():
if path.is_dir():
files.append(cls.from_structure(path))
else:
files.append(
File(path.name, path.read_text(encoding="utf-8").splitlines())
)
return Folder(name, *files)
def __eq__(self, __value: object) -> bool:
if isinstance(__value, File):
return False
if not isinstance(__value, Folder):
return NotImplemented
if self.name != __value.name:
return False
if len(self.files) != len(__value.files):
return False
for self_file, other_file in zip(self.files, __value.files):
if self_file != other_file:
return False
return True
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/cases/imports.py | from tests.unit_tests.migrate.cli_runner.case import Case
from tests.unit_tests.migrate.cli_runner.file import File
cases = [
Case(
name="Imports",
source=File(
"app.py",
content=[
"from langchain_community.chat_models import ChatOpenAI",
"",
"",
"class foo:",
" a: int",
"",
"chain = ChatOpenAI()",
],
),
expected=File(
"app.py",
content=[
"from langchain_openai import ChatOpenAI",
"",
"",
"class foo:",
" a: int",
"",
"chain = ChatOpenAI()",
],
),
),
]
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/cases/imports_with_alias_changes.py | """Handle a test case where the import is updated and may involve an alias change."""
from tests.unit_tests.migrate.cli_runner.case import Case
from tests.unit_tests.migrate.cli_runner.file import File
# The test case right now make sure that if we update the import
# of RunnableMap to RunnableParallel then the code that's using RunnableMap
# should be updated as well (or else we keep importing RunnableMap.)
cases = [
Case(
name="Imports",
source=File(
"app.py",
content=[
"from langchain.runnables import RunnableMap",
"",
"chain = RunnableMap({})",
],
),
expected=File(
"app.py",
content=[
"from langchain_core.runnables import RunnableMap",
"",
"chain = RunnableMap({})",
],
),
),
]
""
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/cli_runner/cases/__init__.py | from tests.unit_tests.migrate.cli_runner.case import Case
from tests.unit_tests.migrate.cli_runner.cases import imports
from tests.unit_tests.migrate.cli_runner.file import File
from tests.unit_tests.migrate.cli_runner.folder import Folder
cases = [
Case(
name="empty",
source=File("__init__.py", content=[]),
expected=File("__init__.py", content=[]),
),
*imports.cases,
]
before = Folder("project", *[case.source for case in cases])
expected = Folder("project", *[case.expected for case in cases])
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/generate/test_langchain_migration.py | import pytest
from langchain._api import suppress_langchain_deprecation_warning as sup2
from langchain_core._api import suppress_langchain_deprecation_warning as sup1
from langchain_cli.namespaces.migrate.generate.generic import (
generate_simplified_migrations,
)
@pytest.mark.xfail(reason="Unknown reason")
def test_create_json_agent_migration() -> None:
"""Test the migration of create_json_agent from langchain to langchain_community."""
with sup1():
with sup2():
raw_migrations = generate_simplified_migrations(
from_package="langchain", to_package="langchain_community"
)
json_agent_migrations = [
migration
for migration in raw_migrations
if "create_json_agent" in migration[0]
]
assert json_agent_migrations == [
(
"langchain.agents.create_json_agent",
"langchain_community.agent_toolkits.create_json_agent",
),
(
"langchain.agents.agent_toolkits.create_json_agent",
"langchain_community.agent_toolkits.create_json_agent",
),
(
"langchain.agents.agent_toolkits.json.base.create_json_agent",
"langchain_community.agent_toolkits.create_json_agent",
),
]
@pytest.mark.xfail(reason="Unknown reason")
def test_create_single_store_retriever_db() -> None:
"""Test migration from langchain to langchain_core"""
with sup1():
with sup2():
raw_migrations = generate_simplified_migrations(
from_package="langchain", to_package="langchain_core"
)
# SingleStore was an old name for VectorStoreRetriever
single_store_migration = [
migration
for migration in raw_migrations
if "SingleStore" in migration[0]
]
assert single_store_migration == [
(
"langchain.vectorstores.singlestoredb.SingleStoreDBRetriever",
"langchain_core.vectorstores.VectorStoreRetriever",
),
]
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/generate/test_partner_migrations.py | import pytest
from langchain_cli.namespaces.migrate.generate.partner import (
get_migrations_for_partner_package,
)
pytest.importorskip(modname="langchain_openai")
def test_generate_migrations() -> None:
migrations = get_migrations_for_partner_package("langchain_openai")
assert migrations == [
("langchain_community.llms.openai.OpenAI", "langchain_openai.OpenAI"),
("langchain_community.llms.openai.AzureOpenAI", "langchain_openai.AzureOpenAI"),
(
"langchain_community.embeddings.openai.OpenAIEmbeddings",
"langchain_openai.OpenAIEmbeddings",
),
(
"langchain_community.embeddings.azure_openai.AzureOpenAIEmbeddings",
"langchain_openai.AzureOpenAIEmbeddings",
),
(
"langchain_community.chat_models.openai.ChatOpenAI",
"langchain_openai.ChatOpenAI",
),
(
"langchain_community.chat_models.azure_openai.AzureChatOpenAI",
"langchain_openai.AzureChatOpenAI",
),
("langchain_community.llms.AzureOpenAI", "langchain_openai.AzureOpenAI"),
("langchain_community.llms.OpenAI", "langchain_openai.OpenAI"),
(
"langchain_community.embeddings.AzureOpenAIEmbeddings",
"langchain_openai.AzureOpenAIEmbeddings",
),
(
"langchain_community.embeddings.OpenAIEmbeddings",
"langchain_openai.OpenAIEmbeddings",
),
(
"langchain_community.chat_models.AzureChatOpenAI",
"langchain_openai.AzureChatOpenAI",
),
("langchain_community.chat_models.ChatOpenAI", "langchain_openai.ChatOpenAI"),
]
|
0 | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate | lc_public_repos/langchain/libs/cli/tests/unit_tests/migrate/generate/test_utils.py | from langchain_cli.namespaces.migrate.generate.utils import PKGS_ROOT
def test_root() -> None:
assert PKGS_ROOT.name == "libs"
|
0 | lc_public_repos/langchain/libs/cli | lc_public_repos/langchain/libs/cli/langchain_cli/dev_scripts.py | # type: ignore
"""
Development Scripts for template packages
"""
from typing import Sequence
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str] = (),
playground_type: str = "default",
):
"""
Creates a demo server for the current template.
"""
app = FastAPI()
package_root = get_package_root()
pyproject = package_root / "pyproject.toml"
try:
package = get_langserve_export(pyproject)
mod = __import__(package["module"], fromlist=[package["attr"]])
chain = getattr(mod, package["attr"])
add_routes(
app,
chain,
config_keys=config_keys,
playground_type=playground_type,
)
except KeyError as e:
raise KeyError("Missing fields from pyproject.toml") from e
except ImportError as e:
raise ImportError("Could not import module defined in pyproject.toml") from e
return app
def create_demo_server_configurable():
return create_demo_server(config_keys=["configurable"])
def create_demo_server_chat():
return create_demo_server(playground_type="chat")
|
0 | lc_public_repos/langchain/libs/cli | lc_public_repos/langchain/libs/cli/langchain_cli/__init__.py | from langchain_cli._version import __version__
__all__ = [
"__version__",
]
|
0 | lc_public_repos/langchain/libs/cli | lc_public_repos/langchain/libs/cli/langchain_cli/cli.py | from typing import Optional
import typer
from typing_extensions import Annotated
from langchain_cli._version import __version__
from langchain_cli.namespaces import app as app_namespace
from langchain_cli.namespaces import integration as integration_namespace
from langchain_cli.namespaces import template as template_namespace
from langchain_cli.namespaces.migrate import main as migrate_namespace
from langchain_cli.utils.packages import get_langserve_export, get_package_root
app = typer.Typer(no_args_is_help=True, add_completion=False)
app.add_typer(
template_namespace.package_cli, name="template", help=template_namespace.__doc__
)
app.add_typer(app_namespace.app_cli, name="app", help=app_namespace.__doc__)
app.add_typer(
integration_namespace.integration_cli,
name="integration",
help=integration_namespace.__doc__,
)
app.command(
name="migrate",
context_settings={
# Let Grit handle the arguments
"allow_extra_args": True,
"ignore_unknown_options": True,
},
)(
migrate_namespace.migrate,
)
def version_callback(show_version: bool) -> None:
if show_version:
typer.echo(f"langchain-cli {__version__}")
raise typer.Exit()
@app.callback()
def main(
version: bool = typer.Option(
False,
"--version",
"-v",
help="Print the current CLI version.",
callback=version_callback,
is_eager=True,
),
):
pass
@app.command()
def serve(
*,
port: Annotated[
Optional[int], typer.Option(help="The port to run the server on")
] = None,
host: Annotated[
Optional[str], typer.Option(help="The host to run the server on")
] = None,
) -> None:
"""
Start the LangServe app, whether it's a template or an app.
"""
# see if is a template
try:
project_dir = get_package_root()
pyproject = project_dir / "pyproject.toml"
get_langserve_export(pyproject)
except KeyError:
# not a template
app_namespace.serve(port=port, host=host)
else:
# is a template
template_namespace.serve(port=port, host=host)
if __name__ == "__main__":
app()
|
0 | lc_public_repos/langchain/libs/cli | lc_public_repos/langchain/libs/cli/langchain_cli/_version.py | from importlib import metadata
try:
__version__ = metadata.version(__package__)
except metadata.PackageNotFoundError:
# Case where package metadata is not available.
__version__ = ""
del metadata # optional, avoids polluting the results of dir(__package__)
__all__ = ["__version__"]
|
0 | lc_public_repos/langchain/libs/cli | lc_public_repos/langchain/libs/cli/langchain_cli/constants.py | DEFAULT_GIT_REPO = "https://github.com/langchain-ai/langchain.git"
DEFAULT_GIT_SUBDIRECTORY = "templates"
DEFAULT_GIT_REF = "master"
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/project_template/Dockerfile | FROM python:3.11-slim
RUN pip install poetry==1.6.1
RUN poetry config virtualenvs.create false
WORKDIR /code
COPY ./pyproject.toml ./README.md ./poetry.lock* ./
COPY ./package[s] ./packages
RUN poetry install --no-interaction --no-ansi --no-root
COPY ./app ./app
RUN poetry install --no-interaction --no-ansi
EXPOSE 8080
CMD exec uvicorn app.server:app --host 0.0.0.0 --port 8080
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/project_template/README.md | # __app_name__
## Installation
Install the LangChain CLI if you haven't yet
```bash
pip install -U langchain-cli
```
## Adding packages
```bash
# adding packages from
# https://github.com/langchain-ai/langchain/tree/master/templates
langchain app add $PROJECT_NAME
# adding custom GitHub repo packages
langchain app add --repo $OWNER/$REPO
# or with whole git string (supports other git providers):
# langchain app add git+https://github.com/hwchase17/chain-of-verification
# with a custom api mount point (defaults to `/{package_name}`)
langchain app add $PROJECT_NAME --api_path=/my/custom/path/rag
```
Note: you remove packages by their api path
```bash
langchain app remove my/custom/path/rag
```
## Setup LangSmith (Optional)
LangSmith will help us trace, monitor and debug LangChain applications.
You can sign up for LangSmith [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```
## Launch LangServe
```bash
langchain serve
```
## Running in Docker
This project folder includes a Dockerfile that allows you to easily build and host your LangServe app.
### Building the Image
To build the image, you simply:
```shell
docker build . -t my-langserve-app
```
If you tag your image with something other than `my-langserve-app`,
note it for use in the next step.
### Running the Image Locally
To run the image, you'll need to include any environment variables
necessary for your application.
In the below example, we inject the `OPENAI_API_KEY` environment
variable with the value set in my local environment
(`$OPENAI_API_KEY`)
We also expose port 8080 with the `-p 8080:8080` option.
```shell
docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -p 8080:8080 my-langserve-app
```
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/project_template/pyproject.toml | [tool.poetry]
name = "__app_name__"
version = "0.1.0"
description = ""
authors = ["Your Name <you@example.com>"]
readme = "README.md"
packages = [
{ include = "app" },
]
[tool.poetry.dependencies]
python = "^3.11"
uvicorn = "^0.23.2"
langserve = {extras = ["server"], version = ">=0.0.30"}
pydantic = "<2"
[tool.poetry.group.dev.dependencies]
langchain-cli = ">=0.0.15"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/project_template | lc_public_repos/langchain/libs/cli/langchain_cli/project_template/app/server.py | from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from langserve import add_routes
app = FastAPI()
@app.get("/")
async def redirect_root_to_docs():
return RedirectResponse("/docs")
# Edit this to add the chain you want to add
add_routes(app, NotImplemented)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/package_template/LICENSE | MIT License
Copyright (c) 2024 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/package_template/README.md | # __package_name__
TODO: What does this package do
## Environment Setup
TODO: What environment variables need to be set (if any)
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package __package_name__
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add __package_name__
```
And add the following code to your `server.py` file:
```python
__app_route_code__
```
(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
You can sign up for LangSmith [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```
If you are inside this directory, then you can spin up a LangServe instance directly by:
```shell
langchain serve
```
This will start the FastAPI app with a server is running locally at
[http://localhost:8000](http://localhost:8000)
We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/__package_name__/playground](http://127.0.0.1:8000/__package_name__/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/__package_name__")
``` |
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/package_template/pyproject.toml | [build-system]
requires = [ "poetry-core",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "__package_name__"
version = "0.0.1"
description = ""
authors = []
readme = "README.md"
[tool.langserve]
export_module = "__module_name__"
export_attr = "chain"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
langchain-openai = ">=0.0.1"
[tool.poetry.group.dev.dependencies]
langchain-cli = ">=0.0.4"
fastapi = "^0.104.0"
sse-starlette = "^1.6.5"
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/package_template | lc_public_repos/langchain/libs/cli/langchain_cli/package_template/package_template/chain.py | from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
_prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful assistant who speaks like a pirate",
),
("human", "{text}"),
]
)
_model = ChatOpenAI()
# if you update this, you MUST also update ../pyproject.toml
# with the new `tool.langserve.export_attr`
chain = _prompt | _model
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/package_template | lc_public_repos/langchain/libs/cli/langchain_cli/package_template/package_template/__init__.py | from __module_name__.chain import chain
__all__ = ["chain"]
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/utils/events.py | import http.client
import json
from typing import Any, Dict, List, Optional, TypedDict
WRITE_KEY = "310apTK0HUFl4AOv"
class EventDict(TypedDict):
event: str
properties: Optional[Dict[str, Any]]
def create_events(events: List[EventDict]) -> Optional[Any]:
try:
data = {
"events": [
{
"write_key": WRITE_KEY,
"name": event["event"],
"properties": event.get("properties"),
}
for event in events
]
}
conn = http.client.HTTPSConnection("app.firstpartyhq.com")
payload = json.dumps(data)
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
}
conn.request("POST", "/events/v1/track/bulk", payload, headers)
res = conn.getresponse()
return json.loads(res.read())
except Exception:
return None
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/utils/git.py | import hashlib
import re
import shutil
from pathlib import Path
from typing import Dict, List, Optional, Sequence, TypedDict
from git import Repo
from langchain_cli.constants import (
DEFAULT_GIT_REF,
DEFAULT_GIT_REPO,
DEFAULT_GIT_SUBDIRECTORY,
)
class DependencySource(TypedDict):
git: str
ref: Optional[str]
subdirectory: Optional[str]
api_path: Optional[str]
event_metadata: Dict
# use poetry dependency string format
def parse_dependency_string(
dep: Optional[str],
repo: Optional[str],
branch: Optional[str],
api_path: Optional[str],
) -> DependencySource:
if dep is not None and dep.startswith("git+"):
if repo is not None or branch is not None:
raise ValueError(
"If a dependency starts with git+, you cannot manually specify "
"a repo or branch."
)
# remove git+
gitstring = dep[4:]
subdirectory = None
ref = None
# first check for #subdirectory= on the end
if "#subdirectory=" in gitstring:
gitstring, subdirectory = gitstring.split("#subdirectory=")
if "#" in subdirectory or "@" in subdirectory:
raise ValueError(
"#subdirectory must be the last part of the dependency string"
)
# find first slash after ://
# find @ or # after that slash
# remainder is ref
# if no @ or #, then ref is None
# find first slash after ://
if "://" not in gitstring:
raise ValueError(
"git+ dependencies must start with git+https:// or git+ssh://"
)
_, find_slash = gitstring.split("://", 1)
if "/" not in find_slash:
post_slash = find_slash
ref = None
else:
_, post_slash = find_slash.split("/", 1)
if "@" in post_slash or "#" in post_slash:
_, ref = re.split(r"[@#]", post_slash, 1)
# gitstring is everything before that
gitstring = gitstring[: -len(ref) - 1] if ref is not None else gitstring
return DependencySource(
git=gitstring,
ref=ref,
subdirectory=subdirectory,
api_path=api_path,
event_metadata={"dependency_string": dep},
)
elif dep is not None and dep.startswith("https://"):
raise ValueError("Only git dependencies are supported")
else:
# if repo is none, use default, including subdirectory
base_subdir = Path(DEFAULT_GIT_SUBDIRECTORY) if repo is None else Path()
subdir = str(base_subdir / dep) if dep is not None else None
gitstring = (
DEFAULT_GIT_REPO
if repo is None
else f"https://github.com/{repo.strip('/')}.git"
)
ref = DEFAULT_GIT_REF if branch is None else branch
# it's a default git repo dependency
return DependencySource(
git=gitstring,
ref=ref,
subdirectory=subdir,
api_path=api_path,
event_metadata={
"dependency_string": dep,
"used_repo_flag": repo is not None,
"used_branch_flag": branch is not None,
},
)
def _list_arg_to_length(arg: Optional[List[str]], num: int) -> Sequence[Optional[str]]:
if not arg:
return [None] * num
elif len(arg) == 1:
return arg * num
elif len(arg) == num:
return arg
else:
raise ValueError(f"Argument must be of length 1 or {num}")
def parse_dependencies(
dependencies: Optional[List[str]],
repo: List[str],
branch: List[str],
api_path: List[str],
) -> List[DependencySource]:
num_deps = max(
len(dependencies) if dependencies is not None else 0, len(repo), len(branch)
)
if (
(dependencies and len(dependencies) != num_deps)
or (api_path and len(api_path) != num_deps)
or (repo and len(repo) not in [1, num_deps])
or (branch and len(branch) not in [1, num_deps])
):
raise ValueError(
"Number of defined repos/branches/api_paths did not match the "
"number of templates."
)
inner_deps = _list_arg_to_length(dependencies, num_deps)
inner_api_paths = _list_arg_to_length(api_path, num_deps)
inner_repos = _list_arg_to_length(repo, num_deps)
inner_branches = _list_arg_to_length(branch, num_deps)
return [
parse_dependency_string(iter_dep, iter_repo, iter_branch, iter_api_path)
for iter_dep, iter_repo, iter_branch, iter_api_path in zip(
inner_deps, inner_repos, inner_branches, inner_api_paths
)
]
def _get_repo_path(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path:
# only based on git for now
ref_str = ref if ref is not None else ""
hashed = hashlib.sha256((f"{gitstring}:{ref_str}").encode("utf-8")).hexdigest()[:8]
removed_protocol = gitstring.split("://")[-1]
removed_basename = re.split(r"[/:]", removed_protocol, 1)[-1]
removed_extras = removed_basename.split("#")[0]
foldername = re.sub(r"\W", "_", removed_extras)
directory_name = f"{foldername}_{hashed}"
return repo_dir / directory_name
def update_repo(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path:
# see if path already saved
repo_path = _get_repo_path(gitstring, ref, repo_dir)
if repo_path.exists():
# try pulling
try:
repo = Repo(repo_path)
if repo.active_branch.name != ref:
raise ValueError()
repo.remotes.origin.pull()
except Exception:
# if it fails, delete and clone again
shutil.rmtree(repo_path)
Repo.clone_from(gitstring, repo_path, branch=ref, depth=1)
else:
Repo.clone_from(gitstring, repo_path, branch=ref, depth=1)
return repo_path
def copy_repo(
source: Path,
destination: Path,
) -> None:
"""
Copies a repo, ignoring git folders.
Raises FileNotFound error if it can't find source
"""
def ignore_func(_, files):
return [f for f in files if f == ".git"]
shutil.copytree(source, destination, ignore=ignore_func)
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/utils/github.py | import http.client
import json
from typing import Optional
def list_packages(*, contains: Optional[str] = None):
conn = http.client.HTTPSConnection("api.github.com")
headers = {
"Accept": "application/vnd.github+json",
"X-GitHub-Api-Version": "2022-11-28",
"User-Agent": "langchain-cli",
}
conn.request(
"GET", "/repos/langchain-ai/langchain/contents/templates", headers=headers
)
res = conn.getresponse()
res_str = res.read()
data = json.loads(res_str)
package_names = [
p["name"] for p in data if p["type"] == "dir" and p["name"] != "docs"
]
package_names_filtered = (
[p for p in package_names if contains in p] if contains else package_names
)
return package_names_filtered
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/utils/find_replace.py | from pathlib import Path
from typing import Dict
def find_and_replace(source: str, replacements: Dict[str, str]) -> str:
rtn = source
# replace keys in deterministic alphabetical order
finds = sorted(replacements.keys())
for find in finds:
replace = replacements[find]
rtn = rtn.replace(find, replace)
return rtn
def replace_file(source: Path, replacements: dict[str, str]) -> None:
try:
content = source.read_text()
except UnicodeDecodeError:
# binary file
return
new_content = find_and_replace(content, replacements)
if new_content != content:
source.write_text(new_content)
def replace_glob(parent: Path, glob: str, replacements: dict[str, str]) -> None:
for file in parent.glob(glob):
if not file.is_file():
continue
replace_file(file, replacements)
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/utils/pyproject.py | from pathlib import Path
from typing import Any, Dict, Iterable, Tuple
from tomlkit import dump, inline_table, load
from tomlkit.items import InlineTable
def _get_dep_inline_table(path: Path) -> InlineTable:
dep = inline_table()
dep.update({"path": str(path), "develop": True})
return dep
def add_dependencies_to_pyproject_toml(
pyproject_toml: Path, local_editable_dependencies: Iterable[Tuple[str, Path]]
) -> None:
"""Add dependencies to pyproject.toml."""
with open(pyproject_toml, encoding="utf-8") as f:
# tomlkit types aren't amazing - treat as Dict instead
pyproject: Dict[str, Any] = load(f)
pyproject["tool"]["poetry"]["dependencies"].update(
{
name: _get_dep_inline_table(loc.relative_to(pyproject_toml.parent))
for name, loc in local_editable_dependencies
}
)
with open(pyproject_toml, "w", encoding="utf-8") as f:
dump(pyproject, f)
def remove_dependencies_from_pyproject_toml(
pyproject_toml: Path, local_editable_dependencies: Iterable[str]
) -> None:
"""Remove dependencies from pyproject.toml."""
with open(pyproject_toml, encoding="utf-8") as f:
pyproject: Dict[str, Any] = load(f)
# tomlkit types aren't amazing - treat as Dict instead
dependencies = pyproject["tool"]["poetry"]["dependencies"]
for name in local_editable_dependencies:
try:
del dependencies[name]
except KeyError:
pass
with open(pyproject_toml, "w", encoding="utf-8") as f:
dump(pyproject, f)
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/utils/packages.py | from pathlib import Path
from typing import Any, Dict, Optional, Set, TypedDict
from tomlkit import load
def get_package_root(cwd: Optional[Path] = None) -> Path:
# traverse path for routes to host (any directory holding a pyproject.toml file)
package_root = Path.cwd() if cwd is None else cwd
visited: Set[Path] = set()
while package_root not in visited:
visited.add(package_root)
pyproject_path = package_root / "pyproject.toml"
if pyproject_path.exists():
return package_root
package_root = package_root.parent
raise FileNotFoundError("No pyproject.toml found")
class LangServeExport(TypedDict):
"""
Fields from pyproject.toml that are relevant to LangServe
Attributes:
module: The module to import from, tool.langserve.export_module
attr: The attribute to import from the module, tool.langserve.export_attr
package_name: The name of the package, tool.poetry.name
"""
module: str
attr: str
package_name: str
def get_langserve_export(filepath: Path) -> LangServeExport:
with open(filepath) as f:
data: Dict[str, Any] = load(f)
try:
module = data["tool"]["langserve"]["export_module"]
attr = data["tool"]["langserve"]["export_attr"]
package_name = data["tool"]["poetry"]["name"]
except KeyError as e:
raise KeyError("Invalid LangServe PyProject.toml") from e
return LangServeExport(module=module, attr=attr, package_name=package_name)
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
# unit tests are run with the --disable-socket flag to prevent network calls
test tests:
poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
# integration tests are run without the --disable-socket flag to allow network calls
integration_test integration_tests:
poetry run pytest $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/__package_name_short__ --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=__module_name__
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find __module_name__ -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/LICENSE | MIT License
Copyright (c) 2024 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/README.md | # __package_name__
This package contains the LangChain integration with __ModuleName__
## Installation
```bash
pip install -U __package_name__
```
And you should configure credentials by setting the following environment variables:
* TODO: fill this out
## Chat Models
`Chat__ModuleName__` class exposes chat models from __ModuleName__.
```python
from __module_name__ import Chat__ModuleName__
llm = Chat__ModuleName__()
llm.invoke("Sing a ballad of LangChain.")
```
## Embeddings
`__ModuleName__Embeddings` class exposes embeddings from __ModuleName__.
```python
from __module_name__ import __ModuleName__Embeddings
embeddings = __ModuleName__Embeddings()
embeddings.embed_query("What is the meaning of life?")
```
## LLMs
`__ModuleName__LLM` class exposes LLMs from __ModuleName__.
```python
from __module_name__ import __ModuleName__LLM
llm = __ModuleName__LLM()
llm.invoke("The meaning of life is")
```
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/pyproject.toml | [build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "__package_name__"
version = "0.1.0"
description = "An integration package connecting __ModuleName__ and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.mypy]
disallow_untyped_defs = "True"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/__package_name_short__"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22__package_name_short__%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
[tool.ruff.lint]
select = ["E", "F", "I", "T201"]
[tool.coverage.run]
omit = ["tests/*"]
[tool.pytest.ini_options]
addopts = "--strict-markers --strict-config --durations=5"
markers = [
"compile: mark placeholder test used to compile integration tests without running them",
]
asyncio_mode = "auto"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
[tool.poetry.group.test.dependencies]
pytest = "^7.4.3"
pytest-asyncio = "^0.23.2"
pytest-socket = "^0.7.0"
pytest-watcher = "^0.3.4"
langchain-tests = "^0.3.5"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.6"
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb | import getpass
import os
if not os.getenv("__MODULE_NAME___API_KEY"):
os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("Enter your __ModuleName__ API key: ")# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")%pip install -qU __package_name__from __module_name__ import __ModuleName__Embeddings
embeddings = __ModuleName__Embeddings(
model="model-name",
)# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore
text = "LangChain is the framework for building context-aware reasoning applications"
vectorstore = InMemoryVectorStore.from_texts(
[text],
embedding=embeddings,
)
# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()
# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")
# show the retrieved document's content
retrieved_documents[0].page_contentsingle_vector = embeddings.embed_query(text)
print(str(single_vector)[:100]) # Show the first 100 characters of the vectortext2 = (
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
print(str(vector)[:100]) # Show the first 100 characters of the vector |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/llms.ipynb | import getpass
import os
if not os.getenv("__MODULE_NAME___API_KEY"):
os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("Enter your __ModuleName__ API key: ")# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")%pip install -qU __package_name__from __module_name__ import __ModuleName__LLM
llm = __ModuleName__LLM(
model="model-name",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# other params...
)input_text = "__ModuleName__ is an AI company that "
completion = llm.invoke(input_text)
completionfrom langchain_core.prompts import PromptTemplate
prompt = PromptTemplate(
"How to say {input} in {output_language}:\n"
)
chain = prompt | llm
chain.invoke(
{
"output_language": "German",
"input": "I love programming.",
}
) |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/chat.ipynb | import getpass
import os
if not os.getenv("__MODULE_NAME___API_KEY"):
os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("Enter your __ModuleName__ API key: ")# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")%pip install -qU __package_name__from __module_name__ import Chat__ModuleName__
llm = Chat__ModuleName__(
model="model-name",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# other params...
)messages = [
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msgprint(ai_msg.content)from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate(
[
(
"system",
"You are a helpful assistant that translates {input_language} to {output_language}.",
),
("human", "{input}"),
]
)
chain = prompt | llm
chain.invoke(
{
"input_language": "English",
"output_language": "German",
"input": "I love programming.",
}
) |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/document_loaders.ipynb | import getpass
import os
os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("Enter your __ModuleName__ API key: ")# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"%pip install -qU langchain_communityfrom langchain_community.document_loaders import __ModuleName__Loader
loader = __ModuleName__Loader(
# required params = ...
# optional params = ...
)docs = loader.load()
docs[0]print(docs[0].metadata)page = []
for doc in loader.lazy_load():
page.append(doc)
if len(page) >= 10:
# do some paged operation, e.g.
# index.upsert(page)
page = [] |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb | import getpass
import os
if not os.getenv("__MODULE_NAME___API_KEY"):
os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("Enter your __ModuleName__ API key: ")# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"from __module_name__.vectorstores import __ModuleName__VectorStore
vector_store = __ModuleName__VectorStore(embeddings=embeddings)from langchain_core.documents import Document
document_1 = Document(
page_content="foo",
metadata={"source": "https://example.com"}
)
document_2 = Document(
page_content="bar",
metadata={"source": "https://example.com"}
)
document_3 = Document(
page_content="baz",
metadata={"source": "https://example.com"}
)
documents = [document_1, document_2, document_3]
vector_store.add_documents(documents=documents,ids=["1","2","3"])updated_document = Document(
page_content="qux",
metadata={"source": "https://another-example.com"}
)
vector_store.update_documents(document_id="1",document=updated_document)vector_store.delete(ids=["3"])results = vector_store.similarity_search(query="thud",k=1,filter={"source":"https://another-example.com"})
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")results = vector_store.similarity_search_with_score(query="thud",k=1,filter={"source":"https://example.com"})
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1}
)
retriever.invoke("thud") |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/stores.ipynb | import getpass
import os
if not os.getenv("__MODULE_NAME___API_KEY"):
os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("Enter your __ModuleName__ API key: ")%pip install -qU __package_name__from __module_name__ import __ModuleName__ByteStore
kv_store = __ModuleName__ByteStore(
# params...
)kv_store.mset(
[
["key1", b"value1"],
["key2", b"value2"],
]
)
kv_store.mget(
[
"key1",
"key2",
]
)kv_store.mdelete(
[
"key1",
"key2",
]
)
kv_store.mget(
[
"key1",
"key2",
]
) |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/tools.ipynb | %pip install --quiet -U langchain-communityimport getpass
import os
# if not os.environ.get("__MODULE_NAME___API_KEY"):
# os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass("__MODULE_NAME__ API key:\n")# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()from langchain_community.tools import __ModuleName__
tool = __ModuleName__(
...
)tool.invoke({...})# This is usually generated by a model, but we'll create a tool call directly for demo purposes.
model_generated_tool_call = {
"args": {...}, # TODO: FILL IN
"id": "1",
"name": tool.name,
"type": "tool_call",
}
tool.invoke(model_generated_tool_call)# | output: false
# | echo: false
# !pip install -qU langchain langchain-openai
from langchain.chat_models import init_chat_model
llm = init_chat_model(model="gpt-4o", model_provider="openai")from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig, chain
prompt = ChatPromptTemplate(
[
("system", "You are a helpful assistant."),
("human", "{user_input}"),
("placeholder", "{messages}"),
]
)
# specifying tool_choice will force the model to call this tool.
llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)
llm_chain = prompt | llm_with_tools
@chain
def tool_chain(user_input: str, config: RunnableConfig):
input_ = {"user_input": user_input}
ai_msg = llm_chain.invoke(input_, config=config)
tool_msgs = tool.batch(ai_msg.tool_calls, config=config)
return llm_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config)
tool_chain.invoke("...") |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/provider.ipynb | from __module_name__ import Chat__ModuleName__
from __module_name__ import __ModuleName__LLM
from __module_name__ import __ModuleName__VectorStore |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/toolkits.ipynb | # os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"%pip install -qU __package_name__from __module_name__ import __ModuleName__Toolkit
toolkit = __ModuleName__Toolkit(
# ...
)toolkit.get_tools()from langgraph.prebuilt import create_react_agent
agent_executor = create_react_agent(llm, tools)example_query = "..."
events = agent_executor.stream(
{"messages": [("user", example_query)]},
stream_mode="values",
)
for event in events:
event["messages"][-1].pretty_print() |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/docs/retrievers.ipynb | # os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"%pip install -qU __package_name__from __module_name__ import __ModuleName__Retriever
retriever = __ModuleName__Retriever(
# ...
)query = "..."
retriever.invoke(query)# | output: false
# | echo: false
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
prompt = ChatPromptTemplate.from_template(
"""Answer the question based only on the context provided.
Context: {context}
Question: {question}"""
)
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)chain.invoke("...") |
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_vectorstores.py | from typing import AsyncGenerator, Generator
import pytest
from __module_name__.vectorstores import __ModuleName__VectorStore
from langchain_core.vectorstores import VectorStore
from langchain_tests.integration_tests import (
AsyncReadWriteTestSuite,
ReadWriteTestSuite,
)
class Test__ModuleName__VectorStoreSync(ReadWriteTestSuite):
@pytest.fixture()
def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore
"""Get an empty vectorstore for unit tests."""
store = __ModuleName__VectorStore()
# note: store should be EMPTY at this point
# if you need to delete data, you may do so here
try:
yield store
finally:
# cleanup operations, or deleting data
pass
class Test__ModuleName__VectorStoreAsync(AsyncReadWriteTestSuite):
@pytest.fixture()
async def vectorstore(self) -> AsyncGenerator[VectorStore, None]: # type: ignore
"""Get an empty vectorstore for unit tests."""
store = __ModuleName__VectorStore()
# note: store should be EMPTY at this point
# if you need to delete data, you may do so here
try:
yield store
finally:
# cleanup operations, or deleting data
pass
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_tools.py | from typing import Type
from __module_name__.tools import __ModuleName__Tool
from langchain_tests.integration_tests import ToolsIntegrationTests
class TestParrotMultiplyToolIntegration(ToolsIntegrationTests):
@property
def tool_constructor(self) -> Type[__ModuleName__Tool]:
return __ModuleName__Tool
@property
def tool_constructor_params(self) -> dict:
# if your tool constructor instead required initialization arguments like
# `def __init__(self, some_arg: int):`, you would return those here
# as a dictionary, e.g.: `return {'some_arg': 42}`
return {}
@property
def tool_invoke_params_example(self) -> dict:
"""
Returns a dictionary representing the "args" of an example tool call.
This should NOT be a ToolCall dict - i.e. it should not
have {"name", "id", "args"} keys.
"""
return {"a": 2, "b": 3}
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_chat_models.py | """Test Chat__ModuleName__ chat model."""
from typing import Type
from __module_name__.chat_models import Chat__ModuleName__
from langchain_tests.integration_tests import ChatModelIntegrationTests
class TestChatParrotLinkIntegration(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[Chat__ModuleName__]:
return Chat__ModuleName__
@property
def chat_model_params(self) -> dict:
# These should be parameters used to initialize your integration for testing
return {
"model": "bird-brain-001",
"temperature": 0,
"parrot_buffer_length": 50,
}
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_embeddings.py | """Test __ModuleName__ embeddings."""
from typing import Type
from __module_name__.embeddings import __ModuleName__Embeddings
from langchain_tests.integration_tests import EmbeddingsIntegrationTests
class TestParrotLinkEmbeddingsIntegration(EmbeddingsIntegrationTests):
@property
def embeddings_class(self) -> Type[__ModuleName__Embeddings]:
return __ModuleName__Embeddings
@property
def embedding_model_params(self) -> dict:
return {"model": "nest-embed-001"}
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_compile.py | import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/integration_tests/test_retrievers.py | from typing import Type
from __module_name__.retrievers import __ModuleName__Retriever
from langchain_tests.integration_tests import (
RetrieversIntegrationTests,
)
class Test__ModuleName__Retriever(RetrieversIntegrationTests):
@property
def retriever_constructor(self) -> Type[__ModuleName__Retriever]:
"""Get an empty vectorstore for unit tests."""
return __ModuleName__Retriever
@property
def retriever_constructor_params(self) -> dict:
return {"k": 2}
@property
def retriever_query_example(self) -> str:
"""
Returns a dictionary representing the "args" of an example retriever call.
"""
return "example query"
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_tools.py | from typing import Type
from __module_name__.tools import __ModuleName__Tool
from langchain_tests.unit_tests import ToolsUnitTests
class TestParrotMultiplyToolUnit(ToolsUnitTests):
@property
def tool_constructor(self) -> Type[__ModuleName__Tool]:
return __ModuleName__Tool
@property
def tool_constructor_params(self) -> dict:
# if your tool constructor instead required initialization arguments like
# `def __init__(self, some_arg: int):`, you would return those here
# as a dictionary, e.g.: `return {'some_arg': 42}`
return {}
@property
def tool_invoke_params_example(self) -> dict:
"""
Returns a dictionary representing the "args" of an example tool call.
This should NOT be a ToolCall dict - i.e. it should not
have {"name", "id", "args"} keys.
"""
return {"a": 2, "b": 3}
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_chat_models.py | """Test chat model integration."""
from typing import Type
from __module_name__.chat_models import Chat__ModuleName__
from langchain_tests.unit_tests import ChatModelUnitTests
class TestChat__ModuleName__Unit(ChatModelUnitTests):
@property
def chat_model_class(self) -> Type[Chat__ModuleName__]:
return Chat__ModuleName__
@property
def chat_model_params(self) -> dict:
# These should be parameters used to initialize your integration for testing
return {
"model": "bird-brain-001",
"temperature": 0,
"parrot_buffer_length": 50,
}
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py | """Test embedding model integration."""
from typing import Type
from __module_name__.embeddings import __ModuleName__Embeddings
from langchain_tests.unit_tests import EmbeddingsUnitTests
class TestParrotLinkEmbeddingsUnit(EmbeddingsUnitTests):
@property
def embeddings_class(self) -> Type[__ModuleName__Embeddings]:
return __ModuleName__Embeddings
@property
def embedding_model_params(self) -> dict:
return {"model": "nest-embed-001"}
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py | """__ModuleName__ document loader."""
from typing import Iterator
from langchain_core.document_loaders.base import BaseLoader
from langchain_core.documents import Document
class __ModuleName__Loader(BaseLoader):
# TODO: Replace all TODOs in docstring. See example docstring:
# https://github.com/langchain-ai/langchain/blob/869523ad728e6b76d77f170cce13925b4ebc3c1e/libs/community/langchain_community/document_loaders/recursive_url_loader.py#L54
"""
__ModuleName__ document loader integration
# TODO: Replace with relevant packages, env vars.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
.. code-block:: bash
pip install -U __package_name__
export __MODULE_NAME___API_KEY="your-api-key"
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
from langchain_community.document_loaders import __ModuleName__Loader
loader = __ModuleName__Loader(
# required params = ...
# other params = ...
)
Lazy load:
.. code-block:: python
docs = []
docs_lazy = loader.lazy_load()
# async variant:
# docs_lazy = await loader.alazy_load()
for doc in docs_lazy:
docs.append(doc)
print(docs[0].page_content[:100])
print(docs[0].metadata)
.. code-block:: python
TODO: Example output
# TODO: Delete if async load is not implemented
Async load:
.. code-block:: python
docs = await loader.aload()
print(docs[0].page_content[:100])
print(docs[0].metadata)
.. code-block:: python
TODO: Example output
""" # noqa: E501
# TODO: This method must be implemented to load documents.
# Do not implement load(), a default implementation is already available.
def lazy_load(self) -> Iterator[Document]:
raise NotImplementedError()
# TODO: Implement if you would like to change default BaseLoader implementation
# async def alazy_load(self) -> AsyncIterator[Document]:
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/integration_template/toolkits.py | """__ModuleName__ toolkits."""
from typing import List
from langchain_core.tools import BaseTool, BaseToolkit
class __ModuleName__Toolkit(BaseToolkit):
# TODO: Replace all TODOs in docstring. See example docstring:
# https://github.com/langchain-ai/langchain/blob/c123cb2b304f52ab65db4714eeec46af69a861ec/libs/community/langchain_community/agent_toolkits/sql/toolkit.py#L19
"""__ModuleName__ toolkit.
# TODO: Replace with relevant packages, env vars, etc.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
.. code-block:: bash
pip install -U __package_name__
export __MODULE_NAME___API_KEY="your-api-key"
# TODO: Populate with relevant params.
Key init args:
arg 1: type
description
arg 2: type
description
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
from __package_name__ import __ModuleName__Toolkit
toolkit = __ModuleName__Toolkit(
# ...
)
Tools:
.. code-block:: python
toolkit.get_tools()
.. code-block:: none
# TODO: Example output.
Use within an agent:
.. code-block:: python
from langgraph.prebuilt import create_react_agent
agent_executor = create_react_agent(llm, tools)
example_query = "..."
events = agent_executor.stream(
{"messages": [("user", example_query)]},
stream_mode="values",
)
for event in events:
event["messages"][-1].pretty_print()
.. code-block:: none
# TODO: Example output.
""" # noqa: E501
# TODO: This method must be implemented to list tools.
def get_tools(self) -> List[BaseTool]:
raise NotImplementedError()
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py | """__ModuleName__ retrievers."""
from typing import Any, List
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class __ModuleName__Retriever(BaseRetriever):
# TODO: Replace all TODOs in docstring. See example docstring:
# https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/retrievers/tavily_search_api.py#L17
"""__ModuleName__ retriever.
# TODO: Replace with relevant packages, env vars, etc.
Setup:
Install ``__package_name__`` and set environment variable
``__MODULE_NAME___API_KEY``.
.. code-block:: bash
pip install -U __package_name__
export __MODULE_NAME___API_KEY="your-api-key"
# TODO: Populate with relevant params.
Key init args:
arg 1: type
description
arg 2: type
description
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
from __package_name__ import __ModuleName__Retriever
retriever = __ModuleName__Retriever(
# ...
)
Usage:
.. code-block:: python
query = "..."
retriever.invoke(query)
.. code-block:: none
# TODO: Example output.
Use within a chain:
.. code-block:: python
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
prompt = ChatPromptTemplate.from_template(
\"\"\"Answer the question based only on the context provided.
Context: {context}
Question: {question}\"\"\"
)
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
def format_docs(docs):
return "\\n\\n".join(doc.page_content for doc in docs)
chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
chain.invoke("...")
.. code-block:: none
# TODO: Example output.
"""
k: int = 3
# TODO: This method must be implemented to retrieve documents.
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
) -> List[Document]:
k = kwargs.get("k", self.k)
return [
Document(page_content=f"Result {i} for query: {query}") for i in range(k)
]
# optional: add custom async implementations here
# async def _aget_relevant_documents(
# self,
# query: str,
# *,
# run_manager: AsyncCallbackManagerForRetrieverRun,
# **kwargs: Any,
# ) -> List[Document]: ...
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py | """__ModuleName__ chat models."""
from typing import Any, Dict, Iterator, List, Optional
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
)
from langchain_core.messages.ai import UsageMetadata
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from pydantic import Field
class Chat__ModuleName__(BaseChatModel):
# TODO: Replace all TODOs in docstring. See example docstring:
# https://github.com/langchain-ai/langchain/blob/7ff05357bac6eaedf5058a2af88f23a1817d40fe/libs/partners/openai/langchain_openai/chat_models/base.py#L1120
"""__ModuleName__ chat model integration.
The default implementation echoes the first `parrot_buffer_length` characters of the input.
# TODO: Replace with relevant packages, env vars.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
.. code-block:: bash
pip install -U __package_name__
export __MODULE_NAME___API_KEY="your-api-key"
# TODO: Populate with relevant params.
Key init args — completion params:
model: str
Name of __ModuleName__ model to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
# TODO: Populate with relevant params.
Key init args — client params:
timeout: Optional[float]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
__ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.
See full list of supported init args and their descriptions in the params section.
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
from __module_name__ import Chat__ModuleName__
llm = Chat__ModuleName__(
model="...",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# api_key="...",
# other params...
)
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
# TODO: Example output.
# TODO: Delete if token-level streaming isn't supported.
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
# TODO: Example output.
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
# TODO: Example output.
# TODO: Delete if native async isn't supported.
Async:
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
.. code-block:: python
# TODO: Example output.
# TODO: Delete if .bind_tools() isn't supported.
Tool calling:
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls
.. code-block:: python
# TODO: Example output.
See ``Chat__ModuleName__.bind_tools()`` method for more.
# TODO: Delete if .with_structured_output() isn't supported.
Structured output:
.. code-block:: python
from typing import Optional
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
# TODO: Example output.
See ``Chat__ModuleName__.with_structured_output()`` for more.
# TODO: Delete if JSON mode response format isn't supported.
JSON mode:
.. code-block:: python
# TODO: Replace with appropriate bind arg.
json_llm = llm.bind(response_format={"type": "json_object"})
ai_msg = json_llm.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
ai_msg.content
.. code-block:: python
# TODO: Example output.
# TODO: Delete if image inputs aren't supported.
Image input:
.. code-block:: python
import base64
import httpx
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
# TODO: Replace with appropriate message content format.
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
],
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
# TODO: Example output.
# TODO: Delete if audio inputs aren't supported.
Audio input:
.. code-block:: python
# TODO: Example input
.. code-block:: python
# TODO: Example output
# TODO: Delete if video inputs aren't supported.
Video input:
.. code-block:: python
# TODO: Example input
.. code-block:: python
# TODO: Example output
# TODO: Delete if token usage metadata isn't supported.
Token usage:
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
# TODO: Delete if logprobs aren't supported.
Logprobs:
.. code-block:: python
# TODO: Replace with appropriate bind arg.
logprobs_llm = llm.bind(logprobs=True)
ai_msg = logprobs_llm.invoke(messages)
ai_msg.response_metadata["logprobs"]
.. code-block:: python
# TODO: Example output.
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
# TODO: Example output.
""" # noqa: E501
model_name: str = Field(alias="model")
"""The name of the model"""
parrot_buffer_length: int
"""The number of characters from the last message of the prompt to be echoed."""
temperature: Optional[float] = None
max_tokens: Optional[int] = None
timeout: Optional[int] = None
stop: Optional[List[str]] = None
max_retries: int = 2
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "chat-__package_name_short__"
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Return a dictionary of identifying parameters.
This information is used by the LangChain callback system, which
is used for tracing purposes make it possible to monitor LLMs.
"""
return {
# The model name allows users to specify custom token counting
# rules in LLM monitoring applications (e.g., in LangSmith users
# can provide per token pricing for their model and monitor
# costs for the given LLM.)
"model_name": self.model_name,
}
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Override the _generate method to implement the chat model logic.
This can be a call to an API, a call to a local model, or any other
implementation that generates a response to the input prompt.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
# Replace this with actual logic to generate a response from a list
# of messages.
last_message = messages[-1]
tokens = last_message.content[: self.parrot_buffer_length]
ct_input_tokens = sum(len(message.content) for message in messages)
ct_output_tokens = len(tokens)
message = AIMessage(
content=tokens,
additional_kwargs={}, # Used to add additional payload to the message
response_metadata={ # Use for response metadata
"time_in_seconds": 3,
},
usage_metadata={
"input_tokens": ct_input_tokens,
"output_tokens": ct_output_tokens,
"total_tokens": ct_input_tokens + ct_output_tokens,
},
)
##
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""Stream the output of the model.
This method should be implemented if the model can generate output
in a streaming fashion. If the model does not support streaming,
do not implement it. In that case streaming requests will be automatically
handled by the _generate method.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
last_message = messages[-1]
tokens = str(last_message.content[: self.parrot_buffer_length])
ct_input_tokens = sum(len(message.content) for message in messages)
for token in tokens:
usage_metadata = UsageMetadata(
{
"input_tokens": ct_input_tokens,
"output_tokens": 1,
"total_tokens": ct_input_tokens + 1,
}
)
ct_input_tokens = 0
chunk = ChatGenerationChunk(
message=AIMessageChunk(content=token, usage_metadata=usage_metadata)
)
if run_manager:
# This is optional in newer versions of LangChain
# The on_llm_new_token will be called automatically
run_manager.on_llm_new_token(token, chunk=chunk)
yield chunk
# Let's add some other information (e.g., response metadata)
chunk = ChatGenerationChunk(
message=AIMessageChunk(content="", response_metadata={"time_in_sec": 3})
)
if run_manager:
# This is optional in newer versions of LangChain
# The on_llm_new_token will be called automatically
run_manager.on_llm_new_token(token, chunk=chunk)
yield chunk
# TODO: Implement if Chat__ModuleName__ supports async streaming. Otherwise delete.
# async def _astream(
# self,
# messages: List[BaseMessage],
# stop: Optional[List[str]] = None,
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> AsyncIterator[ChatGenerationChunk]:
# TODO: Implement if Chat__ModuleName__ supports async generation. Otherwise delete.
# async def _agenerate(
# self,
# messages: List[BaseMessage],
# stop: Optional[List[str]] = None,
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> ChatResult:
|
0 | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template | lc_public_repos/langchain/libs/cli/langchain_cli/integration_template/integration_template/tools.py | """__ModuleName__ tools."""
from typing import Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
class __ModuleName__ToolInput(BaseModel):
"""Input schema for __ModuleName__ tool.
This docstring is **not** part of what is sent to the model when performing tool
calling. The Field default values and descriptions **are** part of what is sent to
the model when performing tool calling.
"""
# TODO: Add input args and descriptions.
a: int = Field(..., description="first number to add")
b: int = Field(..., description="second number to add")
class __ModuleName__Tool(BaseTool): # type: ignore[override]
"""__ModuleName__ tool.
Setup:
# TODO: Replace with relevant packages, env vars.
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
.. code-block:: bash
pip install -U __package_name__
export __MODULE_NAME___API_KEY="your-api-key"
Instantiation:
.. code-block:: python
tool = __ModuleName__Tool(
# TODO: init params
)
Invocation with args:
.. code-block:: python
# TODO: invoke args
tool.invoke({...})
.. code-block:: python
# TODO: output of invocation
Invocation with ToolCall:
.. code-block:: python
# TODO: invoke args
tool.invoke({"args": {...}, "id": "1", "name": tool.name, "type": "tool_call"})
.. code-block:: python
# TODO: output of invocation
""" # noqa: E501
# TODO: Set tool name and description
name: str = "TODO: Tool name"
"""The name that is passed to the model when performing tool calling."""
description: str = "TODO: Tool description."
"""The description that is passed to the model when performing tool calling."""
args_schema: Type[BaseModel] = __ModuleName__ToolInput
"""The schema that is passed to the model when performing tool calling."""
# TODO: Add any other init params for the tool.
# param1: Optional[str]
# """param1 determines foobar"""
# TODO: Replaced (a, b) with real tool arguments.
def _run(
self, a: int, b: int, *, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
return str(a + b + 80)
# TODO: Implement if tool has native async functionality, otherwise delete.
# async def _arun(
# self,
# a: int,
# b: int,
# *,
# run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
# ) -> str:
# ...
|
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